lab 5

Michelle Knopp

2025-04-29

Introduction

Diff-in-Diff models

We will be utilizing diff-in-diff models to analyze the impacts of the New Markets Tax Credit (NMTC) and the Low Income Housing Tax Credit (LIHTC) programs as they relate to social vulnerability and economic changes in Mountain Division tracts.

Diff-In-Diff models are useful as a statistical tool to analyze the effects of a program when there is a treatment group and a group who did not receive the treatment (control group). It can be used when there are two periods, before intervention and after intervention. Since we have tracts who did receive NMTC and LIHTC dollars and tracts who did not, we can analyze the impact of these programs before and after intervention.

Dependent Variables: SVI Variables, House Price Index, Median Home Values, and Median Income

These variables were chosen as dependent variables to look at the impact of our tax programs. The social vulnerability index looks at 4 categories of interest that the CDC has determined impacts overall vulnerability of communities. It is broken down in the following categories: socioeconomic status, household characteristics, racial & ethnic minority status, and housing type/transportation. We will also be looking at economic variables. The house price index is determined by analyzing mortgage transactions. The median home value and median incomes will be collected by census data.

Independent Variables: NMTC and LIHTC Data

New Markets Tax Credits are awarded to community development entities for the purpose of investing in low income communities and recipients must meet strict criteria to be eligible, but the credits are intended to for areas with low median income and high poverty rates.

Low Income Housing Tax Credits are awarded to investors with the purpose of investing in affordable housing for renters. Again, this program is designed to improve neighborhood with low gross incomes and high poverty rates.

Library

# Load packages
library(here)         # relative filepaths for reproducibility
library(rio)          # read excel file from URL
library(tidyverse)    # data wrangling
library(stringi)      # string data wrangling
library(tidycensus)   # US census data
library(ggplot2)      # data visualization
library(kableExtra)   # table formatting
library(scales)       # palette and number formatting
library(unhcrthemes)  # data visualization themes
library(ggrepel)      # data visualization formatting to avoid overlapping
library(rcompanion)   # data visualization of variable distribution
library(ggpubr)       # data visualization of variable distribution
library(moments)      # measures of skewness and kurtosis
library(tinytable)    # format regression tables
library(modelsummary) # format regression tables

Load Functions

import::here( "fips_census_regions",
              "load_svi_data",
              "merge_svi_data",
              "census_division",
              "slopegraph_plot",
              "census_pull",
             # notice the use of here::here() that points to the .R file
             # where all these R objects are created
             .from = here::here("analysis/project_data_steps_knopp.R"),
             .character_only = TRUE)
# Load API key, assign to TidyCensus Package
source(here::here("analysis/password.R"))
census_api_key(census_api_key)

Data

# Load NMTC AND LIHTC data sets

svi_divisional_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_nmtc.rds")))

svi_national_nmtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_nmtc.rds")))

svi_divisional_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_divisional_lihtc.rds")))

svi_national_lihtc <- readRDS(here::here(paste0("data/wrangling/", str_replace_all(census_division, " ", "_"), "_svi_national_lihtc.rds")))

View NMTC Data

*Divisional**

svi_divisional_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag
04001942600 04 001 942600 AZ Arizona Apache County 4 West Region 8 Mountain Division 1561 762 384 1150 1561 73.67072 0.9944 1 26 300 8.666667 0.6866 0 65 366 17.759563 0.10180 0 5 18 27.77778 0.19090 0 70 384 18.22917 0.05781 0 303 839 36.11442 0.9335 1 282 1578 17.87072 0.5921 0 153 9.801409 0.4496 0 560 35.87444 0.9044 1 240 1054 22.770398 0.9006 1 107 332 32.22892 0.9163 1 168 1431 11.740042 0.8831 1 1561 1561 100.00000 0.9989 1 762 0 0.0000000 0.1526 0 215 28.21522 0.9088 1 117 384 30.46875 0.9979 1 33 384 8.59375 0.7842 1 0 1561 0.000000 0.3955 0 3.26441 0.7248 2 4.0540 0.9853 4 0.9989 0.9931 1 3.2390 0.8004 3 11.55631 0.8966 10 1711 676 469 930 1711 54.35418 0.9708 1 44 484 9.090909 0.8539 1 32 456 7.017544 0.02013 0 4 13 30.76923 0.24630 0 36 469 7.675906 0.005758 0 304 1197 25.39683 0.9056 1 686 1711 40.09351 0.9973 1 229 13.38399 0.4397 0 347 20.28054 0.3788 0 245 1363.979 17.962156 0.68240 0 49 304.000 16.11842 0.5859 0 155 1652 9.382567 0.8951 1 1711 1710.980 100.00115 1.0000 1 676 0 0.0000000 0.1276 0 142 21.00592 0.8736 1 83 469 17.697228 0.9774 1 99 469.000 21.10874 0.9655 1 0 1711 0.0000000 0.2155 0 3.733358 0.8474 4 2.98190 0.7375 1 1.0000 0.9958 1 3.1596 0.7653 3 10.87486 0.8573 9 Yes 0 0 $0 0 0 $0 0
04001942700 04 001 942700 AZ Arizona Apache County 4 West Region 8 Mountain Division 4886 2757 1291 2616 4871 53.70560 0.9480 1 163 1398 11.659514 0.8577 1 102 1113 9.164421 0.01757 0 54 178 30.33708 0.22790 0 156 1291 12.08366 0.01652 0 1039 2931 35.44865 0.9303 1 1873 5249 35.68299 0.9436 1 688 14.081048 0.6870 0 1530 31.31396 0.7718 1 772 3514 21.969266 0.8839 1 246 939 26.19808 0.8308 1 592 4631 12.783416 0.8975 1 4846 4886 99.18133 0.9946 1 2757 0 0.0000000 0.1526 0 369 13.38411 0.7652 1 240 1291 18.59024 0.9756 1 188 1291 14.56235 0.9015 1 0 4886 0.000000 0.3955 0 3.69612 0.8288 4 4.0710 0.9870 4 0.9946 0.9890 1 3.1904 0.7848 3 11.95212 0.9295 12 5469 2222 1462 2784 5469 50.90510 0.9557 1 358 1642 21.802680 0.9925 1 114 1151 9.904431 0.04797 0 58 311 18.64952 0.09477 0 172 1462 11.764706 0.023990 0 852 3274 26.02321 0.9120 1 1856 5466 33.95536 0.9919 1 759 13.87822 0.4657 0 1555 28.43299 0.7739 1 706 3911.002 18.051640 0.68720 0 257 1035.000 24.83091 0.8039 1 396 5078 7.798346 0.8624 1 5420 5469.002 99.10401 0.9946 1 2222 0 0.0000000 0.1276 0 400 18.00180 0.8488 1 238 1462 16.279070 0.9710 1 175 1462.001 11.96990 0.8742 1 26 5469 0.4754068 0.6430 0 3.876090 0.8796 4 3.59310 0.9421 3 0.9946 0.9905 1 3.4646 0.8721 3 11.92839 0.9425 11 Yes 0 0 $0 0 0 $0 0
04001944000 04 001 944000 AZ Arizona Apache County 4 West Region 8 Mountain Division 5958 2178 1275 3112 5958 52.23229 0.9399 1 107 1895 5.646438 0.4130 0 108 880 12.272727 0.03476 0 112 395 28.35443 0.19940 0 220 1275 17.25490 0.04955 0 1030 3376 30.50948 0.9015 1 2632 5821 45.21560 0.9873 1 472 7.922122 0.3301 0 1792 30.07721 0.7211 0 299 4027 7.424882 0.1343 0 272 979 27.78345 0.8590 1 153 5325 2.873239 0.6096 0 5846 5958 98.12017 0.9893 1 2178 0 0.0000000 0.1526 0 448 20.56933 0.8562 1 247 1275 19.37255 0.9798 1 135 1275 10.58824 0.8373 1 0 5958 0.000000 0.3955 0 3.29125 0.7314 3 2.6541 0.5792 1 0.9893 0.9836 1 3.2214 0.7946 3 10.15605 0.7714 8 6583 2464 1836 3270 6580 49.69605 0.9486 1 191 2029 9.413504 0.8663 1 89 1272 6.996855 0.01965 0 103 564 18.26241 0.09073 0 192 1836 10.457516 0.015550 0 753 4321 17.42652 0.8100 1 2993 6580 45.48632 0.9992 1 1034 15.70712 0.5561 0 1569 23.83412 0.5584 0 1069 5014.189 21.319499 0.81410 1 304 1237.278 24.57006 0.7989 1 141 6193 2.276764 0.6147 0 6436 6583.375 97.76141 0.9876 1 2464 20 0.8116883 0.3404 0 536 21.75325 0.8793 1 274 1836 14.923747 0.9643 1 326 1836.376 17.75235 0.9488 1 3 6583 0.0455719 0.4382 0 3.639650 0.8211 4 3.34220 0.8770 2 0.9876 0.9834 1 3.5710 0.9020 3 11.54045 0.9156 10 Yes 0 0 $0 0 0 $0 0
04001944100 04 001 944100 AZ Arizona Apache County 4 West Region 8 Mountain Division 4975 2485 1204 3251 4968 65.43881 0.9846 1 210 1254 16.746412 0.9576 1 122 905 13.480663 0.04383 0 91 299 30.43478 0.22960 0 213 1204 17.69103 0.05320 0 779 2325 33.50538 0.9203 1 1293 5511 23.46217 0.7705 1 344 6.914573 0.2701 0 1993 40.06030 0.9701 1 577 3087 18.691286 0.7799 1 278 893 31.13102 0.9038 1 308 4470 6.890380 0.7895 1 4915 4975 98.79397 0.9929 1 2485 21 0.8450704 0.3700 0 428 17.22334 0.8203 1 257 1204 21.34551 0.9843 1 212 1204 17.60797 0.9391 1 0 4975 0.000000 0.3955 0 3.68620 0.8261 4 3.7134 0.9528 4 0.9929 0.9872 1 3.5092 0.8926 3 11.90170 0.9244 12 6183 2379 1424 3704 5789 63.98342 0.9912 1 425 1608 26.430348 0.9954 1 132 1163 11.349957 0.07802 0 38 261 14.55939 0.06498 0 170 1424 11.938202 0.026300 0 862 3259 26.44983 0.9148 1 1320 6183 21.34886 0.9283 1 637 10.30244 0.2718 0 1869 30.22804 0.8396 1 626 3964.000 15.792129 0.57150 0 371 991.000 37.43693 0.9557 1 315 5717 5.509883 0.8021 1 5981 6182.998 96.73300 0.9841 1 2379 0 0.0000000 0.1276 0 442 18.57924 0.8550 1 379 1424 26.615168 0.9969 1 347 1424.000 24.36798 0.9758 1 394 6183 6.3723112 0.9380 1 3.856000 0.8749 4 3.44070 0.9070 3 0.9841 0.9800 1 3.8933 0.9609 4 12.17410 0.9549 12 Yes 0 0 $0 0 0 $0 0
04001944202 04 001 944202 AZ Arizona Apache County 4 West Region 8 Mountain Division 3330 1463 897 1814 3330 54.47447 0.9514 1 345 1024 33.691406 0.9983 1 58 745 7.785235 0.01352 0 38 152 25.00000 0.15680 0 96 897 10.70234 0.01191 0 742 2041 36.35473 0.9351 1 1201 3754 31.99254 0.9089 1 366 10.990991 0.5201 0 873 26.21622 0.5389 0 573 2986 19.189551 0.8002 1 151 550 27.45455 0.8540 1 173 3057 5.659143 0.7527 1 3306 3330 99.27928 0.9948 1 1463 0 0.0000000 0.1526 0 355 24.26521 0.8840 1 114 897 12.70903 0.9435 1 257 897 28.65106 0.9864 1 93 3330 2.792793 0.8680 1 3.80561 0.8512 4 3.4659 0.8981 3 0.9948 0.9891 1 3.8345 0.9589 4 12.10081 0.9410 12 3507 1508 1209 2113 3507 60.25093 0.9862 1 145 1041 13.928914 0.9605 1 81 1040 7.788462 0.02620 0 26 169 15.38462 0.07170 0 107 1209 8.850290 0.008637 0 403 2250 17.91111 0.8195 1 1457 3507 41.54548 0.9985 1 390 11.12062 0.3153 0 974 27.77303 0.7446 0 114 2533.000 4.500592 0.01399 0 189 717.000 26.35983 0.8350 1 389 3265 11.914242 0.9273 1 3499 3507.000 99.77188 0.9983 1 1508 26 1.7241379 0.4052 0 434 28.77984 0.9188 1 98 1209 8.105873 0.8737 1 146 1209.000 12.07610 0.8761 1 0 3507 0.0000000 0.2155 0 3.773337 0.8552 4 2.83619 0.6678 2 0.9983 0.9941 1 3.2893 0.8112 3 10.89713 0.8589 10 Yes 0 0 $0 0 0 $0 0
04001944300 04 001 944300 AZ Arizona Apache County 4 West Region 8 Mountain Division 6806 3308 1826 4099 6797 60.30602 0.9762 1 403 1777 22.678672 0.9858 1 154 1457 10.569664 0.02549 0 63 369 17.07317 0.08684 0 217 1826 11.88390 0.01536 0 1432 3367 42.53044 0.9623 1 2305 7092 32.50141 0.9160 1 746 10.960917 0.5176 0 2767 40.65530 0.9761 1 842 4361 19.307498 0.8041 1 357 1163 30.69647 0.8982 1 568 6178 9.193914 0.8423 1 6750 6806 99.17720 0.9944 1 3308 8 0.2418380 0.3113 0 440 13.30109 0.7638 1 404 1826 22.12486 0.9856 1 388 1826 21.24863 0.9627 1 139 6806 2.042316 0.8458 1 3.85566 0.8602 4 4.0383 0.9844 4 0.9944 0.9888 1 3.8692 0.9619 4 12.75756 0.9749 13 5922 2801 2026 3548 5916 59.97295 0.9854 1 67 1402 4.778887 0.5316 0 251 1664 15.084135 0.20570 0 46 362 12.70718 0.05498 0 297 2026 14.659427 0.056430 0 844 3696 22.83550 0.8792 1 2528 5916 42.73158 0.9987 1 793 13.39075 0.4401 0 1663 28.08173 0.7575 1 573 4258.743 13.454674 0.42530 0 301 1112.258 27.06206 0.8474 1 851 5568 15.283764 0.9575 1 5880 5922.449 99.28326 0.9964 1 2801 22 0.7854338 0.3369 0 521 18.60050 0.8557 1 267 2026 13.178677 0.9482 1 297 2025.690 14.66167 0.9158 1 11 5922 0.1857481 0.5222 0 3.451330 0.7773 3 3.42780 0.9008 3 0.9964 0.9922 1 3.5788 0.9040 3 11.45433 0.9088 10 Yes 0 0 $0 0 0 $0 0

National

svi_national_nmtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag
01001020200 01 001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.57540 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.30190 0 154 730 21.09589 0.09312 0 339 1265 26.79842 0.8392 1 313 2012 15.55666 0.6000 0 204 10.09901 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.8351 1 15 1890 0.7936508 0.40130 0 1243 2020 61.53465 0.7781 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.780822 0.5406 0 115 730 15.753425 0.8382 1 0 2020 0.0000 0.3640 0 2.70312 0.5665 1 3.27660 0.8614 3 0.7781 0.7709 1 2.5316 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.41363 0.6427 0 29 717 4.044630 0.4132 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.40410 0 139 1313 10.58644 0.5601 0 91 1533 5.936073 0.4343 0 284 16.163916 0.5169 0 325 18.49744 0.2851 0 164 1208.000 13.57616 0.4127 0 42 359.0000 11.699164 0.3998 0 0 1651 0.0000000 0.09479 0 1116 1757.000 63.51736 0.7591 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.4688 0 57 573.000 9.947644 0.7317 0 212 1757 12.0660216 0.9549 1 2.45440 0.4888 0 1.70929 0.1025 0 0.7591 0.7527 1 2.9130 0.6862 1 7.83579 0.4802 2 Yes 0 0 $0 0 0 $0 0
01001020700 01 001 020700 AL Alabama Autauga County 3 South Region 6 East South Central Division 2664 1254 1139 710 2664 26.65165 0.6328 0 29 1310 2.213741 0.05255 0 134 710 18.87324 0.13890 0 187 429 43.58974 0.47090 0 321 1139 28.18262 0.28130 0 396 1852 21.38229 0.7478 0 345 2878 11.98749 0.4459 0 389 14.60210 0.6417 0 599 22.48499 0.4007 0 510 2168 23.52399 0.8752 1 228 712 32.022472 0.8712 1 0 2480 0.0000000 0.09298 0 694 2664 26.05105 0.5138 0 1254 8 0.6379585 0.2931 0 460 36.6826156 0.9714 1 0 1139 0.000000 0.1238 0 125 1139 10.974539 0.7477 0 0 2664 0.0000 0.3640 0 2.16035 0.4069 0 2.88178 0.6997 2 0.5138 0.5090 0 2.5000 0.4882 1 8.05593 0.5185 3 3562 1313 1248 1370 3528 38.83220 0.8512 1 128 1562 8.194622 0.7935 1 168 844 19.905213 0.44510 0 237 404 58.66337 0.8359 1 405 1248 32.45192 0.60420 0 396 2211 17.91045 0.7857 1 444 3547 12.517620 0.7758 1 355 9.966311 0.1800 0 954 26.78271 0.7923 1 629 2593.000 24.25762 0.8730 1 171 797.0000 21.455458 0.7186 0 0 3211 0.0000000 0.09479 0 1009 3562.000 28.32678 0.4668 0 1313 14 1.0662605 0.3165 0 443 33.7395278 0.9663 1 73 1248 5.8493590 0.8211 1 17 1248.000 1.362180 0.1554 0 112 3562 3.1443010 0.8514 1 3.81040 0.8569 4 2.65869 0.5847 2 0.4668 0.4629 0 3.1107 0.7714 3 10.04659 0.7851 9 Yes 0 0 $0 0 0 $0 0
01001021100 01 001 021100 AL Alabama Autauga County 3 South Region 6 East South Central Division 3298 1502 1323 860 3298 26.07641 0.6211 0 297 1605 18.504673 0.94340 1 250 1016 24.60630 0.32070 0 74 307 24.10423 0.11920 0 324 1323 24.48980 0.17380 0 710 2231 31.82429 0.8976 1 654 3565 18.34502 0.7018 0 411 12.46210 0.5001 0 738 22.37720 0.3934 0 936 2861 32.71583 0.9807 1 138 825 16.727273 0.5715 0 9 3155 0.2852615 0.25010 0 1979 3298 60.00606 0.7703 1 1502 14 0.9320905 0.3234 0 659 43.8748336 0.9849 1 44 1323 3.325775 0.7062 0 137 1323 10.355253 0.7313 0 0 3298 0.0000 0.3640 0 3.33770 0.7351 2 2.69580 0.6028 1 0.7703 0.7631 1 3.1098 0.7827 1 9.91360 0.7557 5 3499 1825 1462 1760 3499 50.30009 0.9396 1 42 966 4.347826 0.4539 0 426 1274 33.437991 0.85200 1 52 188 27.65957 0.1824 0 478 1462 32.69494 0.61110 0 422 2488 16.96141 0.7638 1 497 3499 14.204058 0.8246 1 853 24.378394 0.8688 1 808 23.09231 0.5829 0 908 2691.100 33.74084 0.9808 1 179 811.6985 22.052524 0.7323 0 8 3248 0.2463054 0.26220 0 1986 3498.713 56.76373 0.7175 0 1825 29 1.5890411 0.3551 0 576 31.5616438 0.9594 1 88 1462 6.0191518 0.8269 1 148 1461.993 10.123166 0.7364 0 38 3499 1.0860246 0.7013 0 3.59300 0.8073 3 3.42700 0.9156 2 0.7175 0.7114 0 3.5791 0.9216 2 11.31660 0.9150 7 Yes 0 0 $0 0 0 $0 0
01003010200 01 003 010200 AL Alabama Baldwin County 3 South Region 6 East South Central Division 2612 1220 1074 338 2605 12.97505 0.2907 0 44 1193 3.688181 0.14720 0 172 928 18.53448 0.13090 0 31 146 21.23288 0.09299 0 203 1074 18.90130 0.05657 0 455 1872 24.30556 0.8016 1 456 2730 16.70330 0.6445 0 401 15.35222 0.6847 0 563 21.55436 0.3406 0 410 2038 20.11776 0.7755 1 64 779 8.215661 0.2181 0 0 2510 0.0000000 0.09298 0 329 2612 12.59571 0.3113 0 1220 38 3.1147541 0.4648 0 385 31.5573770 0.9545 1 20 1074 1.862197 0.5509 0 43 1074 4.003724 0.4088 0 0 2612 0.0000 0.3640 0 1.94057 0.3398 1 2.11188 0.2802 1 0.3113 0.3084 0 2.7430 0.6129 1 7.10675 0.3771 3 2928 1312 1176 884 2928 30.19126 0.7334 0 29 1459 1.987663 0.1356 0 71 830 8.554217 0.03726 0 134 346 38.72832 0.3964 0 205 1176 17.43197 0.12010 0 294 2052 14.32749 0.6940 0 219 2925 7.487179 0.5423 0 556 18.989071 0.6705 0 699 23.87295 0.6339 0 489 2226.455 21.96317 0.8122 1 191 783.8820 24.365914 0.7799 1 0 2710 0.0000000 0.09479 0 398 2927.519 13.59513 0.2511 0 1312 13 0.9908537 0.3111 0 400 30.4878049 0.9557 1 6 1176 0.5102041 0.2590 0 81 1176.202 6.886570 0.6115 0 7 2928 0.2390710 0.4961 0 2.22540 0.4183 0 2.99129 0.7634 2 0.2511 0.2490 0 2.6334 0.5496 1 8.10119 0.5207 3 Yes 0 0 $0 1 408000 $408,000 1
01003010500 01 003 010500 AL Alabama Baldwin County 3 South Region 6 East South Central Division 4230 1779 1425 498 3443 14.46413 0.3337 0 166 1625 10.215385 0.71790 0 151 1069 14.12535 0.04638 0 196 356 55.05618 0.73830 0 347 1425 24.35088 0.17010 0 707 2945 24.00679 0.7967 1 528 4001 13.19670 0.5005 0 619 14.63357 0.6436 0 790 18.67612 0.1937 0 536 3096 17.31266 0.6572 0 165 920 17.934783 0.6102 0 20 4021 0.4973887 0.32320 0 754 4230 17.82506 0.4023 0 1779 97 5.4525014 0.5525 0 8 0.4496908 0.4600 0 63 1425 4.421053 0.7762 1 90 1425 6.315790 0.5691 0 787 4230 18.6052 0.9649 1 2.51890 0.5121 1 2.42790 0.4539 0 0.4023 0.3986 0 3.3227 0.8628 2 8.67180 0.6054 3 5877 1975 1836 820 5244 15.63692 0.3902 0 90 2583 3.484321 0.3361 0 159 1345 11.821561 0.10530 0 139 491 28.30957 0.1924 0 298 1836 16.23094 0.09053 0 570 4248 13.41808 0.6669 0 353 5247 6.727654 0.4924 0 1109 18.870172 0.6645 0 1144 19.46571 0.3411 0 717 4102.545 17.47696 0.6332 0 103 1286.1180 8.008596 0.2341 0 0 5639 0.0000000 0.09479 0 868 5877.481 14.76823 0.2709 0 1975 26 1.3164557 0.3359 0 45 2.2784810 0.6271 0 9 1836 0.4901961 0.2540 0 116 1835.798 6.318779 0.5811 0 633 5877 10.7708014 0.9507 1 1.97613 0.3410 0 1.96769 0.1961 0 0.2709 0.2686 0 2.7488 0.6077 1 6.96352 0.3406 1 Yes 0 0 $0 0 0 $0 0
01003010600 01 003 010600 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3724 1440 1147 1973 3724 52.98067 0.9342 1 142 1439 9.867964 0.69680 0 235 688 34.15698 0.62950 0 187 459 40.74074 0.40290 0 422 1147 36.79163 0.55150 0 497 1876 26.49254 0.8354 1 511 3661 13.95794 0.5334 0 246 6.60580 0.1481 0 1256 33.72718 0.9305 1 496 2522 19.66693 0.7587 1 274 838 32.696897 0.8779 1 32 3479 0.9198045 0.42810 0 2606 3724 69.97852 0.8184 1 1440 21 1.4583333 0.3683 0 321 22.2916667 0.9036 1 97 1147 8.456844 0.8956 1 167 1147 14.559721 0.8209 1 0 3724 0.0000 0.3640 0 3.55130 0.7859 2 3.14330 0.8145 3 0.8184 0.8108 1 3.3524 0.8725 3 10.86540 0.8550 9 4115 1534 1268 1676 3997 41.93145 0.8814 1 294 1809 16.252073 0.9674 1 341 814 41.891892 0.94320 1 204 454 44.93392 0.5438 0 545 1268 42.98107 0.83620 1 624 2425 25.73196 0.9002 1 994 4115 24.155529 0.9602 1 642 15.601458 0.4841 0 1126 27.36331 0.8175 1 568 2989.000 19.00301 0.7045 0 212 715.0000 29.650350 0.8592 1 56 3825 1.4640523 0.53120 0 2715 4115.000 65.97813 0.7732 1 1534 0 0.0000000 0.1079 0 529 34.4850065 0.9685 1 101 1268 7.9652997 0.8795 1 89 1268.000 7.018927 0.6184 0 17 4115 0.4131227 0.5707 0 4.54540 0.9754 5 3.39650 0.9081 2 0.7732 0.7667 1 3.1450 0.7858 2 11.86010 0.9520 10 Yes 0 0 $0 1 8000000 $8,000,000 1

View LIHTC Data

Divisional

svi_divisional_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility
04001942600 04 001 942600 AZ Arizona Apache County 4 West Region 8 Mountain Division 1561 762 384 1150 1561 73.67072 0.9944 1 26 300 8.666667 0.6866 0 65 366 17.759563 0.10180 0 5 18 27.77778 0.19090 0 70 384 18.22917 0.05781 0 303 839 36.11442 0.9335 1 282 1578 17.87072 0.5921 0 153 9.801409 0.449600 0 560 35.874440 0.90440 1 240 1054 22.770398 0.90060 1 107 332 32.22892 0.9163 1 168 1431 11.7400419 0.8831 1 1561 1561 100.00000 0.9989 1 762 0 0.0000000 0.1526 0 215 28.21522 0.9088 1 117 384 30.468750 0.9979 1 33 384 8.593750 0.7842 1 0 1561 0.000000 0.3955 0 3.26441 0.7248 2 4.054000 0.98530 4 0.9989 0.9931 1 3.2390 0.8004 3 11.556310 0.8966 10 1711 676 469 930 1711 54.35418 0.9708 1 44 484 9.090909 0.8539 1 32 456 7.017544 0.02013 0 4 13 30.76923 0.24630 0 36 469 7.675906 0.005758 0 304 1197 25.396825 0.9056 1 686 1711 40.093513 0.9973 1 229 13.3839860 0.439700 0 347 20.280538 0.37880 0 245 1363.979 17.962156 0.6824 0 49 304.0000 16.11842 0.5859 0 155 1652 9.3825666 0.8951 1 1711 1710.980 100.00115 1.0000 1 676 0 0.0000000 0.1276 0 142 21.0059172 0.8736 1 83 469 17.697228 0.9774 1 99 469.0000 21.108742 0.9655 1 0 1711 0.0000000 0.2155 0 3.733358 0.8474 4 2.981900 0.73750 1 1.0000 0.9958 1 3.1596 0.7653 3 10.87486 0.8573 9 0 0 0 0 0 Yes
04001942700 04 001 942700 AZ Arizona Apache County 4 West Region 8 Mountain Division 4886 2757 1291 2616 4871 53.70560 0.9480 1 163 1398 11.659514 0.8577 1 102 1113 9.164421 0.01757 0 54 178 30.33708 0.22790 0 156 1291 12.08366 0.01652 0 1039 2931 35.44865 0.9303 1 1873 5249 35.68299 0.9436 1 688 14.081048 0.687000 0 1530 31.313958 0.77180 1 772 3514 21.969266 0.88390 1 246 939 26.19808 0.8308 1 592 4631 12.7834161 0.8975 1 4846 4886 99.18133 0.9946 1 2757 0 0.0000000 0.1526 0 369 13.38411 0.7652 1 240 1291 18.590240 0.9756 1 188 1291 14.562355 0.9015 1 0 4886 0.000000 0.3955 0 3.69612 0.8288 4 4.071000 0.98700 4 0.9946 0.9890 1 3.1904 0.7848 3 11.952120 0.9295 12 5469 2222 1462 2784 5469 50.90510 0.9557 1 358 1642 21.802680 0.9925 1 114 1151 9.904431 0.04797 0 58 311 18.64952 0.09477 0 172 1462 11.764706 0.023990 0 852 3274 26.023213 0.9120 1 1856 5466 33.955360 0.9919 1 759 13.8782227 0.465700 0 1555 28.432986 0.77390 1 706 3911.002 18.051640 0.6872 0 257 1035.0004 24.83091 0.8039 1 396 5078 7.7983458 0.8624 1 5420 5469.002 99.10401 0.9946 1 2222 0 0.0000000 0.1276 0 400 18.0018002 0.8488 1 238 1462 16.279070 0.9710 1 175 1462.0007 11.969898 0.8742 1 26 5469 0.4754068 0.6430 0 3.876090 0.8796 4 3.593100 0.94210 3 0.9946 0.9905 1 3.4646 0.8721 3 11.92839 0.9425 11 0 0 0 0 0 Yes
04001944100 04 001 944100 AZ Arizona Apache County 4 West Region 8 Mountain Division 4975 2485 1204 3251 4968 65.43881 0.9846 1 210 1254 16.746412 0.9576 1 122 905 13.480663 0.04383 0 91 299 30.43478 0.22960 0 213 1204 17.69103 0.05320 0 779 2325 33.50538 0.9203 1 1293 5511 23.46217 0.7705 1 344 6.914573 0.270100 0 1993 40.060302 0.97010 1 577 3087 18.691286 0.77990 1 278 893 31.13102 0.9038 1 308 4470 6.8903803 0.7895 1 4915 4975 98.79397 0.9929 1 2485 21 0.8450704 0.3700 0 428 17.22334 0.8203 1 257 1204 21.345515 0.9843 1 212 1204 17.607973 0.9391 1 0 4975 0.000000 0.3955 0 3.68620 0.8261 4 3.713400 0.95280 4 0.9929 0.9872 1 3.5092 0.8926 3 11.901700 0.9244 12 6183 2379 1424 3704 5789 63.98342 0.9912 1 425 1608 26.430348 0.9954 1 132 1163 11.349957 0.07802 0 38 261 14.55939 0.06498 0 170 1424 11.938202 0.026300 0 862 3259 26.449831 0.9148 1 1320 6183 21.348860 0.9283 1 637 10.3024422 0.271800 0 1869 30.228045 0.83960 1 626 3964.000 15.792129 0.5715 0 371 991.0000 37.43693 0.9557 1 315 5717 5.5098828 0.8021 1 5981 6182.998 96.73300 0.9841 1 2379 0 0.0000000 0.1276 0 442 18.5792350 0.8550 1 379 1424 26.615168 0.9969 1 347 1424.0000 24.367977 0.9758 1 394 6183 6.3723112 0.9380 1 3.856000 0.8749 4 3.440700 0.90700 3 0.9841 0.9800 1 3.8933 0.9609 4 12.17410 0.9549 12 0 0 0 0 0 Yes
04001944300 04 001 944300 AZ Arizona Apache County 4 West Region 8 Mountain Division 6806 3308 1826 4099 6797 60.30602 0.9762 1 403 1777 22.678672 0.9858 1 154 1457 10.569664 0.02549 0 63 369 17.07317 0.08684 0 217 1826 11.88390 0.01536 0 1432 3367 42.53044 0.9623 1 2305 7092 32.50141 0.9160 1 746 10.960917 0.517600 0 2767 40.655304 0.97610 1 842 4361 19.307498 0.80410 1 357 1163 30.69647 0.8982 1 568 6178 9.1939139 0.8423 1 6750 6806 99.17720 0.9944 1 3308 8 0.2418380 0.3113 0 440 13.30109 0.7638 1 404 1826 22.124863 0.9856 1 388 1826 21.248631 0.9627 1 139 6806 2.042316 0.8458 1 3.85566 0.8602 4 4.038300 0.98440 4 0.9944 0.9888 1 3.8692 0.9619 4 12.757560 0.9749 13 5922 2801 2026 3548 5916 59.97295 0.9854 1 67 1402 4.778887 0.5316 0 251 1664 15.084135 0.20570 0 46 362 12.70718 0.05498 0 297 2026 14.659427 0.056430 0 844 3696 22.835498 0.8792 1 2528 5916 42.731575 0.9987 1 793 13.3907464 0.440100 0 1663 28.081729 0.75750 1 573 4258.743 13.454674 0.4253 0 301 1112.2581 27.06206 0.8474 1 851 5568 15.2837644 0.9575 1 5880 5922.449 99.28326 0.9964 1 2801 22 0.7854338 0.3369 0 521 18.6004998 0.8557 1 267 2026 13.178677 0.9482 1 297 2025.6898 14.661672 0.9158 1 11 5922 0.1857481 0.5222 0 3.451330 0.7773 3 3.427800 0.90080 3 0.9964 0.9922 1 3.5788 0.9040 3 11.45433 0.9088 10 0 0 0 0 0 Yes
04005000800 04 005 000800 AZ Arizona Coconino County 4 West Region 8 Mountain Division 3912 1200 1057 1511 2859 52.85065 0.9430 1 54 1952 2.766393 0.1150 0 71 192 36.979167 0.73370 0 509 865 58.84393 0.83080 1 580 1057 54.87228 0.96160 1 265 1897 13.96943 0.6489 0 995 3589 27.72360 0.8536 1 121 3.093047 0.062070 0 208 5.316973 0.02835 0 248 3170 7.823344 0.15510 0 53 311 17.04180 0.5919 0 26 3898 0.6670087 0.3063 0 1410 3912 36.04294 0.6285 0 1200 155 12.9166667 0.7329 0 3 0.25000 0.3706 0 31 1057 2.932829 0.6261 0 33 1057 3.122044 0.4682 0 1043 3912 26.661554 0.9826 1 3.52210 0.7887 3 1.143720 0.02019 0 0.6285 0.6250 0 3.1804 0.7810 1 8.474720 0.5850 4 6428 2343 2163 3238 5850 55.35043 0.9741 1 399 3753 10.631495 0.9047 1 43 312 13.782051 0.15050 0 1188 1850 64.21622 0.93540 1 1231 2162 56.938020 0.988900 1 364 2823 12.894084 0.7116 0 478 5900 8.101695 0.4937 0 262 4.0759179 0.030250 0 634 9.863099 0.06202 0 497 5227.333 9.507716 0.1782 0 112 544.6422 20.56396 0.7139 0 56 6207 0.9022072 0.4074 0 2862 6428.175 44.52274 0.6490 0 2343 838 35.7661118 0.9165 1 11 0.4694836 0.4053 0 116 2163 5.362922 0.7808 1 166 2162.6681 7.675704 0.7658 1 759 6428 11.8077162 0.9625 1 4.073000 0.9190 3 1.391770 0.04857 0 0.6490 0.6463 0 3.8309 0.9524 4 9.94467 0.7611 7 0 0 0 0 0 Yes
04005001000 04 005 001000 AZ Arizona Coconino County 4 West Region 8 Mountain Division 7519 863 763 1197 1744 68.63532 0.9894 1 1067 4202 25.392670 0.9925 1 17 25 68.000000 0.99610 1 484 738 65.58266 0.91810 1 501 763 65.66186 0.99650 1 47 886 5.30474 0.2676 0 1429 8331 17.15280 0.5641 0 0 0.000000 0.003736 0 310 4.122889 0.02165 0 54 1560 3.461539 0.01727 0 23 174 13.21839 0.4495 0 233 7411 3.1439752 0.6314 0 2495 7519 33.18260 0.5941 0 863 441 51.1008111 0.9666 1 35 4.05562 0.6079 0 14 763 1.834862 0.4856 0 119 763 15.596330 0.9127 1 5775 7519 76.805426 0.9946 1 3.81010 0.8520 3 1.123556 0.01848 0 0.5941 0.5907 0 3.9674 0.9733 3 9.495156 0.7036 6 13499 815 675 1056 1313 80.42650 0.9987 1 1353 6344 21.327238 0.9918 1 22 35 62.857143 0.99810 1 500 641 78.00312 0.99310 1 522 676 77.218935 0.999600 1 29 460 6.304348 0.4346 0 1051 13483 7.795001 0.4716 0 17 0.1259353 0.004211 0 221 1.637158 0.01474 0 125 1282.667 9.745322 0.1874 0 42 114.3578 36.72685 0.9505 1 207 13491 1.5343562 0.5203 0 4803 13498.825 35.58088 0.5539 0 815 550 67.4846626 0.9864 1 7 0.8588957 0.4653 0 62 675 9.185185 0.8933 1 134 675.3319 19.842095 0.9607 1 12185 13499 90.2659456 0.9960 1 3.896300 0.8850 3 1.677151 0.11070 1 0.5539 0.5516 0 4.3017 0.9882 4 10.42905 0.8107 8 0 0 0 0 0 Yes

National

svi_divisional_lihtc %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt FIPS_st FIPS_county FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility
04001942600 04 001 942600 AZ Arizona Apache County 4 West Region 8 Mountain Division 1561 762 384 1150 1561 73.67072 0.9944 1 26 300 8.666667 0.6866 0 65 366 17.759563 0.10180 0 5 18 27.77778 0.19090 0 70 384 18.22917 0.05781 0 303 839 36.11442 0.9335 1 282 1578 17.87072 0.5921 0 153 9.801409 0.449600 0 560 35.874440 0.90440 1 240 1054 22.770398 0.90060 1 107 332 32.22892 0.9163 1 168 1431 11.7400419 0.8831 1 1561 1561 100.00000 0.9989 1 762 0 0.0000000 0.1526 0 215 28.21522 0.9088 1 117 384 30.468750 0.9979 1 33 384 8.593750 0.7842 1 0 1561 0.000000 0.3955 0 3.26441 0.7248 2 4.054000 0.98530 4 0.9989 0.9931 1 3.2390 0.8004 3 11.556310 0.8966 10 1711 676 469 930 1711 54.35418 0.9708 1 44 484 9.090909 0.8539 1 32 456 7.017544 0.02013 0 4 13 30.76923 0.24630 0 36 469 7.675906 0.005758 0 304 1197 25.396825 0.9056 1 686 1711 40.093513 0.9973 1 229 13.3839860 0.439700 0 347 20.280538 0.37880 0 245 1363.979 17.962156 0.6824 0 49 304.0000 16.11842 0.5859 0 155 1652 9.3825666 0.8951 1 1711 1710.980 100.00115 1.0000 1 676 0 0.0000000 0.1276 0 142 21.0059172 0.8736 1 83 469 17.697228 0.9774 1 99 469.0000 21.108742 0.9655 1 0 1711 0.0000000 0.2155 0 3.733358 0.8474 4 2.981900 0.73750 1 1.0000 0.9958 1 3.1596 0.7653 3 10.87486 0.8573 9 0 0 0 0 0 Yes
04001942700 04 001 942700 AZ Arizona Apache County 4 West Region 8 Mountain Division 4886 2757 1291 2616 4871 53.70560 0.9480 1 163 1398 11.659514 0.8577 1 102 1113 9.164421 0.01757 0 54 178 30.33708 0.22790 0 156 1291 12.08366 0.01652 0 1039 2931 35.44865 0.9303 1 1873 5249 35.68299 0.9436 1 688 14.081048 0.687000 0 1530 31.313958 0.77180 1 772 3514 21.969266 0.88390 1 246 939 26.19808 0.8308 1 592 4631 12.7834161 0.8975 1 4846 4886 99.18133 0.9946 1 2757 0 0.0000000 0.1526 0 369 13.38411 0.7652 1 240 1291 18.590240 0.9756 1 188 1291 14.562355 0.9015 1 0 4886 0.000000 0.3955 0 3.69612 0.8288 4 4.071000 0.98700 4 0.9946 0.9890 1 3.1904 0.7848 3 11.952120 0.9295 12 5469 2222 1462 2784 5469 50.90510 0.9557 1 358 1642 21.802680 0.9925 1 114 1151 9.904431 0.04797 0 58 311 18.64952 0.09477 0 172 1462 11.764706 0.023990 0 852 3274 26.023213 0.9120 1 1856 5466 33.955360 0.9919 1 759 13.8782227 0.465700 0 1555 28.432986 0.77390 1 706 3911.002 18.051640 0.6872 0 257 1035.0004 24.83091 0.8039 1 396 5078 7.7983458 0.8624 1 5420 5469.002 99.10401 0.9946 1 2222 0 0.0000000 0.1276 0 400 18.0018002 0.8488 1 238 1462 16.279070 0.9710 1 175 1462.0007 11.969898 0.8742 1 26 5469 0.4754068 0.6430 0 3.876090 0.8796 4 3.593100 0.94210 3 0.9946 0.9905 1 3.4646 0.8721 3 11.92839 0.9425 11 0 0 0 0 0 Yes
04001944100 04 001 944100 AZ Arizona Apache County 4 West Region 8 Mountain Division 4975 2485 1204 3251 4968 65.43881 0.9846 1 210 1254 16.746412 0.9576 1 122 905 13.480663 0.04383 0 91 299 30.43478 0.22960 0 213 1204 17.69103 0.05320 0 779 2325 33.50538 0.9203 1 1293 5511 23.46217 0.7705 1 344 6.914573 0.270100 0 1993 40.060302 0.97010 1 577 3087 18.691286 0.77990 1 278 893 31.13102 0.9038 1 308 4470 6.8903803 0.7895 1 4915 4975 98.79397 0.9929 1 2485 21 0.8450704 0.3700 0 428 17.22334 0.8203 1 257 1204 21.345515 0.9843 1 212 1204 17.607973 0.9391 1 0 4975 0.000000 0.3955 0 3.68620 0.8261 4 3.713400 0.95280 4 0.9929 0.9872 1 3.5092 0.8926 3 11.901700 0.9244 12 6183 2379 1424 3704 5789 63.98342 0.9912 1 425 1608 26.430348 0.9954 1 132 1163 11.349957 0.07802 0 38 261 14.55939 0.06498 0 170 1424 11.938202 0.026300 0 862 3259 26.449831 0.9148 1 1320 6183 21.348860 0.9283 1 637 10.3024422 0.271800 0 1869 30.228045 0.83960 1 626 3964.000 15.792129 0.5715 0 371 991.0000 37.43693 0.9557 1 315 5717 5.5098828 0.8021 1 5981 6182.998 96.73300 0.9841 1 2379 0 0.0000000 0.1276 0 442 18.5792350 0.8550 1 379 1424 26.615168 0.9969 1 347 1424.0000 24.367977 0.9758 1 394 6183 6.3723112 0.9380 1 3.856000 0.8749 4 3.440700 0.90700 3 0.9841 0.9800 1 3.8933 0.9609 4 12.17410 0.9549 12 0 0 0 0 0 Yes
04001944300 04 001 944300 AZ Arizona Apache County 4 West Region 8 Mountain Division 6806 3308 1826 4099 6797 60.30602 0.9762 1 403 1777 22.678672 0.9858 1 154 1457 10.569664 0.02549 0 63 369 17.07317 0.08684 0 217 1826 11.88390 0.01536 0 1432 3367 42.53044 0.9623 1 2305 7092 32.50141 0.9160 1 746 10.960917 0.517600 0 2767 40.655304 0.97610 1 842 4361 19.307498 0.80410 1 357 1163 30.69647 0.8982 1 568 6178 9.1939139 0.8423 1 6750 6806 99.17720 0.9944 1 3308 8 0.2418380 0.3113 0 440 13.30109 0.7638 1 404 1826 22.124863 0.9856 1 388 1826 21.248631 0.9627 1 139 6806 2.042316 0.8458 1 3.85566 0.8602 4 4.038300 0.98440 4 0.9944 0.9888 1 3.8692 0.9619 4 12.757560 0.9749 13 5922 2801 2026 3548 5916 59.97295 0.9854 1 67 1402 4.778887 0.5316 0 251 1664 15.084135 0.20570 0 46 362 12.70718 0.05498 0 297 2026 14.659427 0.056430 0 844 3696 22.835498 0.8792 1 2528 5916 42.731575 0.9987 1 793 13.3907464 0.440100 0 1663 28.081729 0.75750 1 573 4258.743 13.454674 0.4253 0 301 1112.2581 27.06206 0.8474 1 851 5568 15.2837644 0.9575 1 5880 5922.449 99.28326 0.9964 1 2801 22 0.7854338 0.3369 0 521 18.6004998 0.8557 1 267 2026 13.178677 0.9482 1 297 2025.6898 14.661672 0.9158 1 11 5922 0.1857481 0.5222 0 3.451330 0.7773 3 3.427800 0.90080 3 0.9964 0.9922 1 3.5788 0.9040 3 11.45433 0.9088 10 0 0 0 0 0 Yes
04005000800 04 005 000800 AZ Arizona Coconino County 4 West Region 8 Mountain Division 3912 1200 1057 1511 2859 52.85065 0.9430 1 54 1952 2.766393 0.1150 0 71 192 36.979167 0.73370 0 509 865 58.84393 0.83080 1 580 1057 54.87228 0.96160 1 265 1897 13.96943 0.6489 0 995 3589 27.72360 0.8536 1 121 3.093047 0.062070 0 208 5.316973 0.02835 0 248 3170 7.823344 0.15510 0 53 311 17.04180 0.5919 0 26 3898 0.6670087 0.3063 0 1410 3912 36.04294 0.6285 0 1200 155 12.9166667 0.7329 0 3 0.25000 0.3706 0 31 1057 2.932829 0.6261 0 33 1057 3.122044 0.4682 0 1043 3912 26.661554 0.9826 1 3.52210 0.7887 3 1.143720 0.02019 0 0.6285 0.6250 0 3.1804 0.7810 1 8.474720 0.5850 4 6428 2343 2163 3238 5850 55.35043 0.9741 1 399 3753 10.631495 0.9047 1 43 312 13.782051 0.15050 0 1188 1850 64.21622 0.93540 1 1231 2162 56.938020 0.988900 1 364 2823 12.894084 0.7116 0 478 5900 8.101695 0.4937 0 262 4.0759179 0.030250 0 634 9.863099 0.06202 0 497 5227.333 9.507716 0.1782 0 112 544.6422 20.56396 0.7139 0 56 6207 0.9022072 0.4074 0 2862 6428.175 44.52274 0.6490 0 2343 838 35.7661118 0.9165 1 11 0.4694836 0.4053 0 116 2163 5.362922 0.7808 1 166 2162.6681 7.675704 0.7658 1 759 6428 11.8077162 0.9625 1 4.073000 0.9190 3 1.391770 0.04857 0 0.6490 0.6463 0 3.8309 0.9524 4 9.94467 0.7611 7 0 0 0 0 0 Yes
04005001000 04 005 001000 AZ Arizona Coconino County 4 West Region 8 Mountain Division 7519 863 763 1197 1744 68.63532 0.9894 1 1067 4202 25.392670 0.9925 1 17 25 68.000000 0.99610 1 484 738 65.58266 0.91810 1 501 763 65.66186 0.99650 1 47 886 5.30474 0.2676 0 1429 8331 17.15280 0.5641 0 0 0.000000 0.003736 0 310 4.122889 0.02165 0 54 1560 3.461539 0.01727 0 23 174 13.21839 0.4495 0 233 7411 3.1439752 0.6314 0 2495 7519 33.18260 0.5941 0 863 441 51.1008111 0.9666 1 35 4.05562 0.6079 0 14 763 1.834862 0.4856 0 119 763 15.596330 0.9127 1 5775 7519 76.805426 0.9946 1 3.81010 0.8520 3 1.123556 0.01848 0 0.5941 0.5907 0 3.9674 0.9733 3 9.495156 0.7036 6 13499 815 675 1056 1313 80.42650 0.9987 1 1353 6344 21.327238 0.9918 1 22 35 62.857143 0.99810 1 500 641 78.00312 0.99310 1 522 676 77.218935 0.999600 1 29 460 6.304348 0.4346 0 1051 13483 7.795001 0.4716 0 17 0.1259353 0.004211 0 221 1.637158 0.01474 0 125 1282.667 9.745322 0.1874 0 42 114.3578 36.72685 0.9505 1 207 13491 1.5343562 0.5203 0 4803 13498.825 35.58088 0.5539 0 815 550 67.4846626 0.9864 1 7 0.8588957 0.4653 0 62 675 9.185185 0.8933 1 134 675.3319 19.842095 0.9607 1 12185 13499 90.2659456 0.9960 1 3.896300 0.8850 3 1.677151 0.11070 1 0.5539 0.5516 0 4.3017 0.9882 4 10.42905 0.8107 8 0 0 0 0 0 Yes

Housing Price Index Data

hpi_df <- read.csv("https://r-class.github.io/paf-515-course-materials/data/raw/HPI/HPI_AT_BDL_tract.csv")
hpi_df_10_20 <- hpi_df %>% 
  mutate(GEOID10 = str_pad(tract, 11, "left", pad=0)) %>% 
  filter(year %in% c(2010, 2020))  %>%
 select(GEOID10, state_abbr, year, hpi) %>%
  pivot_wider(names_from = year, values_from = hpi) %>%
  mutate(housing_price_index10 = `2010`,
         housing_price_index20 = `2020`) %>%
  select(GEOID10, state_abbr, housing_price_index10, housing_price_index20)

# View data
hpi_df_10_20 %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID10 state_abbr housing_price_index10 housing_price_index20
01001020100 AL 132.35 152.78
01001020200 AL 123.78 123.37
01001020300 AL 158.57 167.01
01001020400 AL 165.11 179.60
01001020501 AL 172.55 180.96
01001020502 AL 158.75 164.25
# Drop state_abbr column for joining
hpi_df_10_20 <- hpi_df_10_20 %>% select(-state_abbr)

Core Based statistical Areas (CBSA) Crosswalk

msa_csa_crosswalk <- rio::import("https://r-class.github.io/paf-515-course-materials/data/raw/CSA_MSA_Crosswalk/qcew-county-msa-csa-crosswalk.xlsx", which=4)

msa_csa_crosswalk <- msa_csa_crosswalk %>% 
  mutate(county_fips = str_pad(`County Code`, 5, "left", pad=0),
         cbsa = coalesce(`CSA Title`, `MSA Title`),
         cbsa_code = coalesce(`CSA Code`, `MSA Code`),
         county_title = `County Title`)  %>% 
  select(county_fips, county_title, cbsa, cbsa_code)

msa_csa_crosswalk %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
county_fips county_title cbsa cbsa_code
01001 Autauga County, Alabama Montgomery-Alexander City, AL CSA CS388
01003 Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01005 Barbour County, Alabama Eufaula, AL-GA MicroSA C2164
01007 Bibb County, Alabama Birmingham-Hoover-Cullman, AL CSA CS142
01009 Blount County, Alabama Birmingham-Hoover-Cullman, AL CSA CS142
01015 Calhoun County, Alabama Anniston-Oxford, AL MSA C1150

Census Data

states <- list(svi_national_nmtc$state %>% unique())
states 
## [[1]]
##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "DC" "FL" "GA" "HI" "ID" "IL" "IN"
## [16] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH"
## [31] "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN" "TX" "UT"
## [46] "VT" "VA" "WA" "WV" "WI" "WY"
census_pull10 <- lapply(states, census_pull, yr = 2010)

census_pull10_df <- census_pull10[[1]] %>%  
  # Drop margin of error column
  select(-moe) %>%
  # Add suffix to variable names
  mutate(variable = paste0(variable, "_10")) %>%
  # Pivot data frame
  pivot_wider(
    names_from = variable,
    values_from = c(estimate)
  )

census_pull10_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID NAME Median_Income_10 Median_Home_Value_10
01001020100 Census Tract 201, Autauga County, Alabama 31769 120700
01001020200 Census Tract 202, Autauga County, Alabama 19437 138500
01001020300 Census Tract 203, Autauga County, Alabama 24146 111300
01001020400 Census Tract 204, Autauga County, Alabama 27735 126300
01001020500 Census Tract 205, Autauga County, Alabama 35517 173000
01001020600 Census Tract 206, Autauga County, Alabama 24597 110700
01001020700 Census Tract 207, Autauga County, Alabama 22114 93800
01001020801 Census Tract 208.01, Autauga County, Alabama 30841 258000
01001020802 Census Tract 208.02, Autauga County, Alabama 29006 145100
01001020900 Census Tract 209, Autauga County, Alabama 24841 108000
census_pull19 <- lapply(states, census_pull, yr = 2019)

census_pull19_df <- census_pull19[[1]] %>% 
  # Select columns
  select(GEOID, NAME, variable, estimate, moe) %>% 
  # Create individual FIPS columns for state, county, and tract
  mutate(FIPS_st = substr(GEOID, 1, 2),
         FIPS_county = substr(GEOID, 3, 5),
         FIPS_tract = substr(GEOID, 6, 11)) %>%
# Los Angeles, CA Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "037" & FIPS_st == "06" & FIPS_tract == "137000"), "930401", FIPS_tract )) %>%
# Pima County, AZ Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002704"), "002701", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "002906"), "002903", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004118"), "410501", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004121"), "410502", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "004125"), "410503", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005200"), "470400", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "019" & FIPS_st == "04" & FIPS_tract == "005300"), "470500", FIPS_tract2 )) %>%
# Madison County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030101"), "940101", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030102"), "940102", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030103"), "940103", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030200"), "940200", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030300"), "940300", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030401"), "940401", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030403"), "940403", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030600"), "940600", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "053" & FIPS_st == "36" & FIPS_tract == "030402"), "940700", FIPS_tract2 )) %>%
# Oneida County, NY Census Tract fixes
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024800"), "940000", FIPS_tract2 )) %>% 
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024700"), "940100", FIPS_tract2 )) %>%
                      mutate(FIPS_tract2 = if_else((FIPS_county == "065" & FIPS_st == "36" & FIPS_tract == "024900"), "940200", FIPS_tract2 )) %>%  
                      # Move columns in data set
                      relocate(c(FIPS_st, FIPS_county, FIPS_tract, FIPS_tract2),.after = GEOID) %>%
                      # Create new GEOID column
                      mutate(GEOID = paste0(FIPS_st, FIPS_county, FIPS_tract2)) %>% 
                      # Drop newly created FIPS columns and margin of error
                      select(-FIPS_st, -FIPS_county, -FIPS_tract, -FIPS_tract2, -moe) %>% 
                      # Add suffix
                      mutate(variable = paste0(variable, "_19")) %>%
                      # Pivot data set
                      pivot_wider(
                        names_from = variable,
                        values_from = c(estimate)
                      ) 

census_pull19_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID NAME Median_Income_19 Median_Home_Value_19
01001020100 Census Tract 201, Autauga County, Alabama 25970 136100
01001020200 Census Tract 202, Autauga County, Alabama 20154 90500
01001020300 Census Tract 203, Autauga County, Alabama 27383 122600
01001020400 Census Tract 204, Autauga County, Alabama 34620 152700
01001020500 Census Tract 205, Autauga County, Alabama 41178 186900
01001020600 Census Tract 206, Autauga County, Alabama 21146 103600
01001020700 Census Tract 207, Autauga County, Alabama 20934 82400
01001020801 Census Tract 208.01, Autauga County, Alabama 31667 322900
01001020802 Census Tract 208.02, Autauga County, Alabama 33086 171500
01001020900 Census Tract 209, Autauga County, Alabama 32677 156900
inflation_adj = 1.16

# Join 2010 and 2019 Median Income and Home Value Data
census_pull_df <- left_join(census_pull10_df, census_pull19_df[c("GEOID", "Median_Income_19", "Median_Home_Value_19")], join_by("GEOID" == "GEOID"))

# Create new inflation adjusted columns for 2010 median income and median home value, find changes over time
census_pull_df <- census_pull_df %>% 
                   mutate(Median_Income_10adj = Median_Income_10*inflation_adj,
                          Median_Home_Value_10adj = Median_Home_Value_10*inflation_adj,
                          Median_Income_Change = Median_Income_19 - Median_Income_10adj,
                          Median_Income_Change_pct = (Median_Income_19 - Median_Income_10adj)/Median_Income_10adj,
                          Median_Home_Value_Change = Median_Home_Value_19 - Median_Home_Value_10adj,
                          Median_Home_Value_Change_pct = (Median_Home_Value_19 - Median_Home_Value_10adj)/Median_Home_Value_10adj)

# View data
census_pull_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID NAME Median_Income_10 Median_Home_Value_10 Median_Income_19 Median_Home_Value_19 Median_Income_10adj Median_Home_Value_10adj Median_Income_Change Median_Income_Change_pct Median_Home_Value_Change Median_Home_Value_Change_pct
01001020100 Census Tract 201, Autauga County, Alabama 31769 120700 25970 136100 36852.04 140012 -10882.04 -0.2952900 -3912 -0.0279405
01001020200 Census Tract 202, Autauga County, Alabama 19437 138500 20154 90500 22546.92 160660 -2392.92 -0.1061307 -70160 -0.4366986
01001020300 Census Tract 203, Autauga County, Alabama 24146 111300 27383 122600 28009.36 129108 -626.36 -0.0223625 -6508 -0.0504074
01001020400 Census Tract 204, Autauga County, Alabama 27735 126300 34620 152700 32172.60 146508 2447.40 0.0760709 6192 0.0422639
01001020500 Census Tract 205, Autauga County, Alabama 35517 173000 41178 186900 41199.72 200680 -21.72 -0.0005272 -13780 -0.0686665
01001020600 Census Tract 206, Autauga County, Alabama 24597 110700 21146 103600 28532.52 128412 -7386.52 -0.2588807 -24812 -0.1932218
01001020700 Census Tract 207, Autauga County, Alabama 22114 93800 20934 82400 25652.24 108808 -4718.24 -0.1839309 -26408 -0.2427027
01001020801 Census Tract 208.01, Autauga County, Alabama 30841 258000 31667 322900 35775.56 299280 -4108.56 -0.1148426 23620 0.0789227
01001020802 Census Tract 208.02, Autauga County, Alabama 29006 145100 33086 171500 33646.96 168316 -560.96 -0.0166719 3184 0.0189168
01001020900 Census Tract 209, Autauga County, Alabama 24841 108000 32677 156900 28815.56 125280 3861.44 0.1340054 31620 0.2523946

NMTC Data Sets

Divisional

svi_divisional_nmtc_df0 <- left_join(svi_divisional_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_nmtc_df1 <- left_join(svi_divisional_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_nmtc_df <- left_join(svi_divisional_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt county_fips FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag NAME Median_Income_10 Median_Home_Value_10 Median_Income_19 Median_Home_Value_19 Median_Income_10adj Median_Home_Value_10adj Median_Income_Change Median_Income_Change_pct Median_Home_Value_Change Median_Home_Value_Change_pct housing_price_index10 housing_price_index20 county_title cbsa cbsa_code
04001942600 04001 942600 AZ Arizona Apache County 4 West Region 8 Mountain Division 1561 762 384 1150 1561 73.67072 0.9944 1 26 300 8.666667 0.6866 0 65 366 17.759563 0.101800 0 5 18 27.77778 0.19090 0 70 384 18.229167 0.057810 0 303 839 36.11442 0.9335 1 282 1578 17.87072 0.5921 0 153 9.801409 0.4496 0 560 35.87444 0.9044 1 240 1054 22.770398 0.9006 1 107 332 32.22892 0.9163 1 168 1431 11.740042 0.8831 1 1561 1561 100.00000 0.9989 1 762 0 0.0000000 0.1526 0 215 28.21522 0.9088 1 117 384 30.46875 0.9979 1 33 384 8.593750 0.7842 1 0 1561 0.000000 0.3955 0 3.264410 0.7248 2 4.0540 0.9853 4 0.9989 0.9931 1 3.2390 0.8004 3 11.55631 0.8966 10 1711 676 469 930 1711 54.35418 0.9708 1 44 484 9.090909 0.8539 1 32 456 7.017544 0.02013 0 4 13 30.769231 0.24630 0 36 469 7.675906 0.005758 0 304 1197 25.39683 0.9056 1 686 1711 40.09351 0.9973 1 229 13.38399 0.4397 0 347 20.28054 0.3788 0 245 1363.979 17.962156 0.68240 0 49 304.0000 16.11842 0.5859 0 155 1652 9.382567 0.8951 1 1711 1710.980 100.00115 1.0000 1 676 0 0.0000000 0.1276 0 142 21.00592 0.8736 1 83 469 17.697228 0.9774 1 99 469.000 21.10874 0.9655 1 0 1711 0.0000000 0.2155 0 3.733358 0.8474 4 2.98190 0.7375 1 1.0000 0.9958 1 3.1596 0.7653 3 10.87486 0.8573 9 Yes 0 0 $0 0 0 $0 0 Census Tract 9426, Apache County, Arizona 10268 27600 15822 45700 11910.88 32016 3911.12 0.3283653 13684 0.4274113 NA NA NA NA NA
04001942700 04001 942700 AZ Arizona Apache County 4 West Region 8 Mountain Division 4886 2757 1291 2616 4871 53.70560 0.9480 1 163 1398 11.659514 0.8577 1 102 1113 9.164421 0.017570 0 54 178 30.33708 0.22790 0 156 1291 12.083656 0.016520 0 1039 2931 35.44865 0.9303 1 1873 5249 35.68299 0.9436 1 688 14.081048 0.6870 0 1530 31.31396 0.7718 1 772 3514 21.969266 0.8839 1 246 939 26.19808 0.8308 1 592 4631 12.783416 0.8975 1 4846 4886 99.18133 0.9946 1 2757 0 0.0000000 0.1526 0 369 13.38411 0.7652 1 240 1291 18.59024 0.9756 1 188 1291 14.562355 0.9015 1 0 4886 0.000000 0.3955 0 3.696120 0.8288 4 4.0710 0.9870 4 0.9946 0.9890 1 3.1904 0.7848 3 11.95212 0.9295 12 5469 2222 1462 2784 5469 50.90510 0.9557 1 358 1642 21.802680 0.9925 1 114 1151 9.904431 0.04797 0 58 311 18.649518 0.09477 0 172 1462 11.764706 0.023990 0 852 3274 26.02321 0.9120 1 1856 5466 33.95536 0.9919 1 759 13.87822 0.4657 0 1555 28.43299 0.7739 1 706 3911.002 18.051640 0.68720 0 257 1035.0004 24.83091 0.8039 1 396 5078 7.798346 0.8624 1 5420 5469.002 99.10401 0.9946 1 2222 0 0.0000000 0.1276 0 400 18.00180 0.8488 1 238 1462 16.279070 0.9710 1 175 1462.001 11.96990 0.8742 1 26 5469 0.4754068 0.6430 0 3.876090 0.8796 4 3.59310 0.9421 3 0.9946 0.9905 1 3.4646 0.8721 3 11.92839 0.9425 11 Yes 0 0 $0 0 0 $0 0 Census Tract 9427, Apache County, Arizona 14348 55900 18740 47200 16643.68 64844 2096.32 0.1259529 -17644 -0.2720992 NA NA NA NA NA
04001944000 04001 944000 AZ Arizona Apache County 4 West Region 8 Mountain Division 5958 2178 1275 3112 5958 52.23229 0.9399 1 107 1895 5.646438 0.4130 0 108 880 12.272727 0.034760 0 112 395 28.35443 0.19940 0 220 1275 17.254902 0.049550 0 1030 3376 30.50948 0.9015 1 2632 5821 45.21560 0.9873 1 472 7.922122 0.3301 0 1792 30.07721 0.7211 0 299 4027 7.424882 0.1343 0 272 979 27.78345 0.8590 1 153 5325 2.873239 0.6096 0 5846 5958 98.12017 0.9893 1 2178 0 0.0000000 0.1526 0 448 20.56933 0.8562 1 247 1275 19.37255 0.9798 1 135 1275 10.588235 0.8373 1 0 5958 0.000000 0.3955 0 3.291250 0.7314 3 2.6541 0.5792 1 0.9893 0.9836 1 3.2214 0.7946 3 10.15605 0.7714 8 6583 2464 1836 3270 6580 49.69605 0.9486 1 191 2029 9.413504 0.8663 1 89 1272 6.996855 0.01965 0 103 564 18.262411 0.09073 0 192 1836 10.457516 0.015550 0 753 4321 17.42652 0.8100 1 2993 6580 45.48632 0.9992 1 1034 15.70712 0.5561 0 1569 23.83412 0.5584 0 1069 5014.189 21.319499 0.81410 1 304 1237.2784 24.57006 0.7989 1 141 6193 2.276764 0.6147 0 6436 6583.375 97.76141 0.9876 1 2464 20 0.8116883 0.3404 0 536 21.75325 0.8793 1 274 1836 14.923747 0.9643 1 326 1836.376 17.75235 0.9488 1 3 6583 0.0455719 0.4382 0 3.639650 0.8211 4 3.34220 0.8770 2 0.9876 0.9834 1 3.5710 0.9020 3 11.54045 0.9156 10 Yes 0 0 $0 0 0 $0 0 Census Tract 9440, Apache County, Arizona 17679 61100 21541 40000 20507.64 70876 1033.36 0.0503890 -30876 -0.4356341 NA NA NA NA NA
04001944100 04001 944100 AZ Arizona Apache County 4 West Region 8 Mountain Division 4975 2485 1204 3251 4968 65.43881 0.9846 1 210 1254 16.746412 0.9576 1 122 905 13.480663 0.043830 0 91 299 30.43478 0.22960 0 213 1204 17.691030 0.053200 0 779 2325 33.50538 0.9203 1 1293 5511 23.46217 0.7705 1 344 6.914573 0.2701 0 1993 40.06030 0.9701 1 577 3087 18.691286 0.7799 1 278 893 31.13102 0.9038 1 308 4470 6.890380 0.7895 1 4915 4975 98.79397 0.9929 1 2485 21 0.8450704 0.3700 0 428 17.22334 0.8203 1 257 1204 21.34551 0.9843 1 212 1204 17.607973 0.9391 1 0 4975 0.000000 0.3955 0 3.686200 0.8261 4 3.7134 0.9528 4 0.9929 0.9872 1 3.5092 0.8926 3 11.90170 0.9244 12 6183 2379 1424 3704 5789 63.98342 0.9912 1 425 1608 26.430348 0.9954 1 132 1163 11.349957 0.07802 0 38 261 14.559387 0.06498 0 170 1424 11.938202 0.026300 0 862 3259 26.44983 0.9148 1 1320 6183 21.34886 0.9283 1 637 10.30244 0.2718 0 1869 30.22804 0.8396 1 626 3964.000 15.792129 0.57150 0 371 991.0000 37.43693 0.9557 1 315 5717 5.509883 0.8021 1 5981 6182.998 96.73300 0.9841 1 2379 0 0.0000000 0.1276 0 442 18.57924 0.8550 1 379 1424 26.615168 0.9969 1 347 1424.000 24.36798 0.9758 1 394 6183 6.3723112 0.9380 1 3.856000 0.8749 4 3.44070 0.9070 3 0.9841 0.9800 1 3.8933 0.9609 4 12.17410 0.9549 12 Yes 0 0 $0 0 0 $0 0 Census Tract 9441, Apache County, Arizona 13469 60900 16162 46800 15624.04 70644 537.96 0.0344316 -23844 -0.3375234 NA NA NA NA NA
04001944202 04001 944202 AZ Arizona Apache County 4 West Region 8 Mountain Division 3330 1463 897 1814 3330 54.47447 0.9514 1 345 1024 33.691406 0.9983 1 58 745 7.785235 0.013520 0 38 152 25.00000 0.15680 0 96 897 10.702341 0.011910 0 742 2041 36.35473 0.9351 1 1201 3754 31.99254 0.9089 1 366 10.990991 0.5201 0 873 26.21622 0.5389 0 573 2986 19.189551 0.8002 1 151 550 27.45455 0.8540 1 173 3057 5.659143 0.7527 1 3306 3330 99.27928 0.9948 1 1463 0 0.0000000 0.1526 0 355 24.26521 0.8840 1 114 897 12.70903 0.9435 1 257 897 28.651059 0.9864 1 93 3330 2.792793 0.8680 1 3.805610 0.8512 4 3.4659 0.8981 3 0.9948 0.9891 1 3.8345 0.9589 4 12.10081 0.9410 12 3507 1508 1209 2113 3507 60.25093 0.9862 1 145 1041 13.928914 0.9605 1 81 1040 7.788462 0.02620 0 26 169 15.384615 0.07170 0 107 1209 8.850290 0.008637 0 403 2250 17.91111 0.8195 1 1457 3507 41.54548 0.9985 1 390 11.12062 0.3153 0 974 27.77303 0.7446 0 114 2533.000 4.500592 0.01399 0 189 717.0000 26.35983 0.8350 1 389 3265 11.914242 0.9273 1 3499 3507.000 99.77188 0.9983 1 1508 26 1.7241379 0.4052 0 434 28.77984 0.9188 1 98 1209 8.105873 0.8737 1 146 1209.000 12.07610 0.8761 1 0 3507 0.0000000 0.2155 0 3.773337 0.8552 4 2.83619 0.6678 2 0.9983 0.9941 1 3.2893 0.8112 3 10.89713 0.8589 10 Yes 0 0 $0 0 0 $0 0 Census Tract 9442.02, Apache County, Arizona 11741 53100 16052 25400 13619.56 61596 2432.44 0.1785990 -36196 -0.5876356 NA NA NA NA NA
04001944300 04001 944300 AZ Arizona Apache County 4 West Region 8 Mountain Division 6806 3308 1826 4099 6797 60.30602 0.9762 1 403 1777 22.678672 0.9858 1 154 1457 10.569664 0.025490 0 63 369 17.07317 0.08684 0 217 1826 11.883899 0.015360 0 1432 3367 42.53044 0.9623 1 2305 7092 32.50141 0.9160 1 746 10.960917 0.5176 0 2767 40.65530 0.9761 1 842 4361 19.307498 0.8041 1 357 1163 30.69647 0.8982 1 568 6178 9.193914 0.8423 1 6750 6806 99.17720 0.9944 1 3308 8 0.2418380 0.3113 0 440 13.30109 0.7638 1 404 1826 22.12486 0.9856 1 388 1826 21.248631 0.9627 1 139 6806 2.042316 0.8458 1 3.855660 0.8602 4 4.0383 0.9844 4 0.9944 0.9888 1 3.8692 0.9619 4 12.75756 0.9749 13 5922 2801 2026 3548 5916 59.97295 0.9854 1 67 1402 4.778887 0.5316 0 251 1664 15.084135 0.20570 0 46 362 12.707182 0.05498 0 297 2026 14.659427 0.056430 0 844 3696 22.83550 0.8792 1 2528 5916 42.73158 0.9987 1 793 13.39075 0.4401 0 1663 28.08173 0.7575 1 573 4258.743 13.454674 0.42530 0 301 1112.2581 27.06206 0.8474 1 851 5568 15.283764 0.9575 1 5880 5922.449 99.28326 0.9964 1 2801 22 0.7854338 0.3369 0 521 18.60050 0.8557 1 267 2026 13.178677 0.9482 1 297 2025.690 14.66167 0.9158 1 11 5922 0.1857481 0.5222 0 3.451330 0.7773 3 3.42780 0.9008 3 0.9964 0.9922 1 3.5788 0.9040 3 11.45433 0.9088 10 Yes 0 0 $0 0 0 $0 0 Census Tract 9443, Apache County, Arizona 11133 48700 15051 53700 12914.28 56492 2136.72 0.1654541 -2792 -0.0494229 NA NA NA NA NA
04001944901 04001 944901 AZ Arizona Apache County 4 West Region 8 Mountain Division 3538 2001 868 2034 3506 58.01483 0.9664 1 153 926 16.522678 0.9551 1 68 621 10.950081 0.027230 0 47 247 19.02834 0.10240 0 115 868 13.248848 0.023430 0 615 1757 35.00285 0.9280 1 1204 3723 32.33951 0.9127 1 409 11.560204 0.5556 0 1406 39.73997 0.9672 1 598 2467 24.239968 0.9257 1 181 629 28.77583 0.8759 1 244 3213 7.594149 0.8079 1 3491 3538 98.67157 0.9923 1 2001 0 0.0000000 0.1526 0 269 13.44328 0.7659 1 206 868 23.73272 0.9887 1 112 868 12.903226 0.8773 1 0 3538 0.000000 0.3955 0 3.785630 0.8476 4 4.1323 0.9882 4 0.9923 0.9867 1 3.1800 0.7807 3 12.09023 0.9402 12 4008 1775 1127 2545 4008 63.49800 0.9902 1 43 1000 4.300000 0.4727 0 70 946 7.399577 0.02292 0 14 181 7.734807 0.03172 0 84 1127 7.453416 0.005182 0 558 2312 24.13495 0.8942 1 598 4008 14.92016 0.8150 1 446 11.12774 0.3160 0 1361 33.95709 0.9342 1 393 2647.000 14.846997 0.51690 0 276 723.0000 38.17427 0.9599 1 177 3788 4.672650 0.7720 1 3957 4008.000 98.72754 0.9920 1 1775 0 0.0000000 0.1276 0 393 22.14085 0.8824 1 234 1127 20.763088 0.9868 1 280 1127.000 24.84472 0.9785 1 1 4008 0.0249501 0.4340 0 3.177282 0.7088 3 3.49900 0.9216 3 0.9920 0.9878 1 3.4093 0.8520 3 11.07758 0.8758 10 Yes 0 0 $0 0 0 $0 0 Census Tract 9449.01, Apache County, Arizona 13033 60200 20349 38200 15118.28 69832 5230.72 0.3459864 -31632 -0.4529728 NA NA NA NA NA
04001944902 04001 944902 AZ Arizona Apache County 4 West Region 8 Mountain Division 6532 2589 1471 4250 6532 65.06430 0.9841 1 564 1838 30.685528 0.9971 1 94 1267 7.419100 0.012740 0 38 204 18.62745 0.09853 0 132 1471 8.973487 0.007874 0 1496 3893 38.42795 0.9450 1 1637 5606 29.20086 0.8785 1 751 11.497244 0.5527 0 1893 28.98040 0.6741 0 919 3843 23.913609 0.9204 1 217 969 22.39422 0.7518 1 934 6035 15.476388 0.9216 1 6415 6532 98.20882 0.9902 1 2589 7 0.2703747 0.3137 0 347 13.40286 0.7654 1 304 1471 20.66621 0.9833 1 418 1471 28.416044 0.9858 1 767 6532 11.742192 0.9557 1 3.812574 0.8526 4 3.8206 0.9657 3 0.9902 0.9846 1 4.0039 0.9775 4 12.62727 0.9703 12 4952 2210 1419 3101 4952 62.62116 0.9887 1 130 1341 9.694258 0.8776 1 128 1299 9.853734 0.04623 0 11 120 9.166667 0.03614 0 139 1419 9.795631 0.011710 0 759 3361 22.58256 0.8769 1 983 4952 19.85057 0.9107 1 759 15.32714 0.5379 0 1115 22.51616 0.4881 0 720 3837.257 18.763404 0.72230 0 232 934.7419 24.81969 0.8037 1 342 4667 7.328048 0.8515 1 4915 4951.551 99.26182 0.9962 1 2210 0 0.0000000 0.1276 0 404 18.28054 0.8519 1 287 1419 20.225511 0.9850 1 404 1419.310 28.46453 0.9881 1 25 4952 0.5048465 0.6529 0 3.665610 0.8272 4 3.40350 0.8950 2 0.9962 0.9920 1 3.6055 0.9109 3 11.67081 0.9240 10 Yes 0 0 $0 0 0 $0 0 Census Tract 9449.02, Apache County, Arizona 9837 64500 17988 30300 11410.92 74820 6577.08 0.5763847 -44520 -0.5950281 NA NA NA NA NA
04001945001 04001 945001 AZ Arizona Apache County 4 West Region 8 Mountain Division 4746 1477 993 2072 4523 45.81030 0.8983 1 210 1440 14.583333 0.9298 1 23 435 5.287356 0.008689 0 203 558 36.37993 0.34020 0 226 993 22.759315 0.129200 0 272 2249 12.09426 0.5906 0 1244 4576 27.18531 0.8446 1 195 4.108723 0.1071 0 1806 38.05310 0.9454 1 397 3289 12.070538 0.4212 0 294 722 40.72022 0.9743 1 37 4167 0.887929 0.3569 0 4676 4746 98.52507 0.9914 1 1477 0 0.0000000 0.1526 0 365 24.71225 0.8873 1 140 993 14.09869 0.9529 1 36 993 3.625378 0.5120 0 271 4746 5.710072 0.9170 1 3.392500 0.7522 3 2.8049 0.6463 2 0.9914 0.9857 1 3.4218 0.8668 3 10.61060 0.8171 9 4085 1794 1251 1170 3940 29.69543 0.7399 0 56 1640 3.414634 0.3522 0 69 759 9.090909 0.03679 0 22 492 4.471545 0.02268 0 91 1251 7.274181 0.004990 0 364 2717 13.39713 0.7250 0 975 4058 24.02661 0.9538 1 476 11.65239 0.3461 0 1006 24.62668 0.5988 0 484 3059.000 15.822164 0.57440 0 185 787.0000 23.50699 0.7766 1 59 3882 1.519835 0.5168 0 4012 4085.000 98.21297 0.9902 1 1794 0 0.0000000 0.1276 0 395 22.01784 0.8814 1 182 1251 14.548361 0.9626 1 141 1251.000 11.27098 0.8621 1 77 4085 1.8849449 0.8277 1 2.775890 0.5926 1 2.81270 0.6550 1 0.9902 0.9861 1 3.6614 0.9221 4 10.24019 0.7910 7 Yes 0 0 $0 1 12544000 $12,544,000 1 Census Tract 9450.01, Apache County, Arizona 13049 50500 25587 66700 15136.84 58580 10450.16 0.6903792 8120 0.1386139 NA NA NA NA NA
04001945002 04001 945002 AZ Arizona Apache County 4 West Region 8 Mountain Division 4093 1831 1075 2257 4093 55.14293 0.9553 1 66 1026 6.432748 0.4929 0 140 968 14.462810 0.054450 0 21 107 19.62617 0.10760 0 161 1075 14.976744 0.033030 0 698 2337 29.86735 0.8977 1 1357 4258 31.86942 0.9075 1 449 10.969949 0.5188 0 1355 33.10530 0.8284 1 428 3071 13.936828 0.5396 0 173 677 25.55391 0.8189 1 232 3846 6.032241 0.7667 1 4083 4093 99.75568 0.9971 1 1831 0 0.0000000 0.1526 0 333 18.18678 0.8305 1 261 1075 24.27907 0.9898 1 292 1075 27.162791 0.9835 1 0 4093 0.000000 0.3955 0 3.286430 0.7303 3 3.4724 0.8998 3 0.9971 0.9914 1 3.3519 0.8430 3 11.10783 0.8573 10 4053 1729 1156 2013 4050 49.70370 0.9488 1 136 1268 10.725552 0.9064 1 68 1072 6.343284 0.01445 0 9 84 10.714286 0.04296 0 77 1156 6.660900 0.004223 0 607 2836 21.40339 0.8655 1 1166 4053 28.76881 0.9803 1 550 13.57019 0.4498 0 869 21.44091 0.4357 0 488 3184.000 15.326633 0.54540 0 224 763.0000 29.35780 0.8826 1 84 3910 2.148338 0.6005 0 3971 4053.000 97.97681 0.9885 1 1729 0 0.0000000 0.1276 0 471 27.24118 0.9100 1 280 1156 24.221453 0.9944 1 197 1156.000 17.04152 0.9413 1 6 4053 0.1480385 0.4982 0 3.705223 0.8383 4 2.91400 0.7057 1 0.9885 0.9844 1 3.4715 0.8754 3 11.07922 0.8760 9 Yes 0 0 $0 0 0 $0 0 Census Tract 9450.02, Apache County, Arizona 12308 56600 20899 34700 14277.28 65656 6621.72 0.4637942 -30956 -0.4714878 NA NA NA NA NA

National

svi_national_nmtc_df0 <- left_join(svi_national_nmtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_nmtc_df1 <- left_join(svi_national_nmtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_nmtc_df <- left_join(svi_national_nmtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_nmtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt county_fips FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 nmtc_eligibility pre10_nmtc_project_cnt pre10_nmtc_dollars pre10_nmtc_dollars_formatted post10_nmtc_project_cnt post10_nmtc_dollars post10_nmtc_dollars_formatted nmtc_flag NAME Median_Income_10 Median_Home_Value_10 Median_Income_19 Median_Home_Value_19 Median_Income_10adj Median_Home_Value_10adj Median_Income_Change Median_Income_Change_pct Median_Home_Value_Change Median_Home_Value_Change_pct housing_price_index10 housing_price_index20 county_title cbsa cbsa_code
01001020200 01001 020200 AL Alabama Autauga County 3 South Region 6 East South Central Division 2020 816 730 495 1992 24.84940 0.5954 0 68 834 8.153477 0.57540 0 49 439 11.16173 0.02067 0 105 291 36.08247 0.30190 0 154 730 21.09589 0.09312 0 339 1265 26.798419 0.8392 1 313 2012 15.55666 0.6000 0 204 10.09901 0.3419 0 597 29.55446 0.8192 1 359 1515 23.69637 0.8791 1 132 456 28.947368 0.8351 1 15 1890 0.7936508 0.40130 0 1243 2020 61.534653 0.77810 1 816 0 0.0000000 0.1224 0 34 4.1666667 0.6664 0 13 730 1.780822 0.5406 0 115 730 15.7534247 0.83820 1 0 2020 0.0000 0.3640 0 2.70312 0.5665 1 3.27660 0.8614 3 0.77810 0.7709 1 2.53160 0.5047 1 9.28942 0.6832 6 1757 720 573 384 1511 25.413633 0.6427 0 29 717 4.044630 0.41320 0 33 392 8.418367 0.03542 0 116 181 64.08840 0.9086 1 149 573 26.00349 0.40410 0 139 1313 10.586443 0.5601 0 91 1533 5.936073 0.4343 0 284 16.163916 0.5169 0 325 18.49744 0.28510 0 164 1208.000 13.576159 0.4127 0 42 359.0000 11.6991643 0.39980 0 0 1651 0.0000000 0.09479 0 1116 1757.000 63.5173591 0.759100 1 720 3 0.4166667 0.2470 0 5 0.6944444 0.5106 0 9 573 1.5706806 0.46880 0 57 573.000 9.947644 0.7317 0 212 1757 12.0660216 0.9549 1 2.45440 0.4888 0 1.70929 0.10250 0 0.759100 0.752700 1 2.91300 0.6862 1 7.835790 0.4802 2 Yes 0 0 $0 0 0 $0 0 Census Tract 202, Autauga County, Alabama 19437 138500 20154 90500 22546.92 160660 -2392.92 -0.1061307 -70160 -0.4366986 123.78 123.37 Autauga County, Alabama Montgomery-Alexander City, AL CSA CS388
01001020700 01001 020700 AL Alabama Autauga County 3 South Region 6 East South Central Division 2664 1254 1139 710 2664 26.65165 0.6328 0 29 1310 2.213741 0.05255 0 134 710 18.87324 0.13890 0 187 429 43.58974 0.47090 0 321 1139 28.18262 0.28130 0 396 1852 21.382289 0.7478 0 345 2878 11.98749 0.4459 0 389 14.60210 0.6417 0 599 22.48499 0.4007 0 510 2168 23.52399 0.8752 1 228 712 32.022472 0.8712 1 0 2480 0.0000000 0.09298 0 694 2664 26.051051 0.51380 0 1254 8 0.6379585 0.2931 0 460 36.6826156 0.9714 1 0 1139 0.000000 0.1238 0 125 1139 10.9745391 0.74770 0 0 2664 0.0000 0.3640 0 2.16035 0.4069 0 2.88178 0.6997 2 0.51380 0.5090 0 2.50000 0.4882 1 8.05593 0.5185 3 3562 1313 1248 1370 3528 38.832200 0.8512 1 128 1562 8.194622 0.79350 1 168 844 19.905213 0.44510 0 237 404 58.66337 0.8359 1 405 1248 32.45192 0.60420 0 396 2211 17.910448 0.7857 1 444 3547 12.517620 0.7758 1 355 9.966311 0.1800 0 954 26.78271 0.79230 1 629 2593.000 24.257617 0.8730 1 171 797.0000 21.4554580 0.71860 0 0 3211 0.0000000 0.09479 0 1009 3562.000 28.3267827 0.466800 0 1313 14 1.0662605 0.3165 0 443 33.7395278 0.9663 1 73 1248 5.8493590 0.82110 1 17 1248.000 1.362180 0.1554 0 112 3562 3.1443010 0.8514 1 3.81040 0.8569 4 2.65869 0.58470 2 0.466800 0.462900 0 3.11070 0.7714 3 10.046590 0.7851 9 Yes 0 0 $0 0 0 $0 0 Census Tract 207, Autauga County, Alabama 22114 93800 20934 82400 25652.24 108808 -4718.24 -0.1839309 -26408 -0.2427027 95.94 108.47 Autauga County, Alabama Montgomery-Alexander City, AL CSA CS388
01001021100 01001 021100 AL Alabama Autauga County 3 South Region 6 East South Central Division 3298 1502 1323 860 3298 26.07641 0.6211 0 297 1605 18.504673 0.94340 1 250 1016 24.60630 0.32070 0 74 307 24.10423 0.11920 0 324 1323 24.48980 0.17380 0 710 2231 31.824294 0.8976 1 654 3565 18.34502 0.7018 0 411 12.46210 0.5001 0 738 22.37720 0.3934 0 936 2861 32.71583 0.9807 1 138 825 16.727273 0.5715 0 9 3155 0.2852615 0.25010 0 1979 3298 60.006064 0.77030 1 1502 14 0.9320905 0.3234 0 659 43.8748336 0.9849 1 44 1323 3.325775 0.7062 0 137 1323 10.3552532 0.73130 0 0 3298 0.0000 0.3640 0 3.33770 0.7351 2 2.69580 0.6028 1 0.77030 0.7631 1 3.10980 0.7827 1 9.91360 0.7557 5 3499 1825 1462 1760 3499 50.300086 0.9396 1 42 966 4.347826 0.45390 0 426 1274 33.437991 0.85200 1 52 188 27.65957 0.1824 0 478 1462 32.69494 0.61110 0 422 2488 16.961415 0.7638 1 497 3499 14.204058 0.8246 1 853 24.378394 0.8688 1 808 23.09231 0.58290 0 908 2691.100 33.740844 0.9808 1 179 811.6985 22.0525243 0.73230 0 8 3248 0.2463054 0.26220 0 1986 3498.713 56.7637257 0.717500 0 1825 29 1.5890411 0.3551 0 576 31.5616438 0.9594 1 88 1462 6.0191518 0.82690 1 148 1461.993 10.123166 0.7364 0 38 3499 1.0860246 0.7013 0 3.59300 0.8073 3 3.42700 0.91560 2 0.717500 0.711400 0 3.57910 0.9216 2 11.316600 0.9150 7 Yes 0 0 $0 0 0 $0 0 Census Tract 211, Autauga County, Alabama 17997 74000 20620 88600 20876.52 85840 -256.52 -0.0122875 2760 0.0321528 134.13 145.41 Autauga County, Alabama Montgomery-Alexander City, AL CSA CS388
01003010200 01003 010200 AL Alabama Baldwin County 3 South Region 6 East South Central Division 2612 1220 1074 338 2605 12.97505 0.2907 0 44 1193 3.688181 0.14720 0 172 928 18.53448 0.13090 0 31 146 21.23288 0.09299 0 203 1074 18.90130 0.05657 0 455 1872 24.305556 0.8016 1 456 2730 16.70330 0.6445 0 401 15.35222 0.6847 0 563 21.55436 0.3406 0 410 2038 20.11776 0.7755 1 64 779 8.215661 0.2181 0 0 2510 0.0000000 0.09298 0 329 2612 12.595712 0.31130 0 1220 38 3.1147541 0.4648 0 385 31.5573770 0.9545 1 20 1074 1.862197 0.5509 0 43 1074 4.0037244 0.40880 0 0 2612 0.0000 0.3640 0 1.94057 0.3398 1 2.11188 0.2802 1 0.31130 0.3084 0 2.74300 0.6129 1 7.10675 0.3771 3 2928 1312 1176 884 2928 30.191257 0.7334 0 29 1459 1.987663 0.13560 0 71 830 8.554217 0.03726 0 134 346 38.72832 0.3964 0 205 1176 17.43197 0.12010 0 294 2052 14.327485 0.6940 0 219 2925 7.487179 0.5423 0 556 18.989071 0.6705 0 699 23.87295 0.63390 0 489 2226.455 21.963167 0.8122 1 191 783.8820 24.3659136 0.77990 1 0 2710 0.0000000 0.09479 0 398 2927.519 13.5951280 0.251100 0 1312 13 0.9908537 0.3111 0 400 30.4878049 0.9557 1 6 1176 0.5102041 0.25900 0 81 1176.202 6.886570 0.6115 0 7 2928 0.2390710 0.4961 0 2.22540 0.4183 0 2.99129 0.76340 2 0.251100 0.249000 0 2.63340 0.5496 1 8.101190 0.5207 3 Yes 0 0 $0 1 408000 $408,000 1 Census Tract 102, Baldwin County, Alabama 23862 103200 26085 136900 27679.92 119712 -1594.92 -0.0576201 17188 0.1435779 128.38 166.27 Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003010500 01003 010500 AL Alabama Baldwin County 3 South Region 6 East South Central Division 4230 1779 1425 498 3443 14.46413 0.3337 0 166 1625 10.215385 0.71790 0 151 1069 14.12535 0.04638 0 196 356 55.05618 0.73830 0 347 1425 24.35088 0.17010 0 707 2945 24.006791 0.7967 1 528 4001 13.19670 0.5005 0 619 14.63357 0.6436 0 790 18.67612 0.1937 0 536 3096 17.31266 0.6572 0 165 920 17.934783 0.6102 0 20 4021 0.4973887 0.32320 0 754 4230 17.825059 0.40230 0 1779 97 5.4525014 0.5525 0 8 0.4496908 0.4600 0 63 1425 4.421053 0.7762 1 90 1425 6.3157895 0.56910 0 787 4230 18.6052 0.9649 1 2.51890 0.5121 1 2.42790 0.4539 0 0.40230 0.3986 0 3.32270 0.8628 2 8.67180 0.6054 3 5877 1975 1836 820 5244 15.636918 0.3902 0 90 2583 3.484321 0.33610 0 159 1345 11.821561 0.10530 0 139 491 28.30957 0.1924 0 298 1836 16.23094 0.09053 0 570 4248 13.418079 0.6669 0 353 5247 6.727654 0.4924 0 1109 18.870172 0.6645 0 1144 19.46571 0.34110 0 717 4102.545 17.476956 0.6332 0 103 1286.1180 8.0085961 0.23410 0 0 5639 0.0000000 0.09479 0 868 5877.481 14.7682323 0.270900 0 1975 26 1.3164557 0.3359 0 45 2.2784810 0.6271 0 9 1836 0.4901961 0.25400 0 116 1835.798 6.318779 0.5811 0 633 5877 10.7708014 0.9507 1 1.97613 0.3410 0 1.96769 0.19610 0 0.270900 0.268600 0 2.74880 0.6077 1 6.963520 0.3406 1 Yes 0 0 $0 0 0 $0 0 Census Tract 105, Baldwin County, Alabama 21585 121100 28301 148500 25038.60 140476 3262.40 0.1302948 8024 0.0571201 191.57 213.49 Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003010600 01003 010600 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3724 1440 1147 1973 3724 52.98067 0.9342 1 142 1439 9.867964 0.69680 0 235 688 34.15698 0.62950 0 187 459 40.74074 0.40290 0 422 1147 36.79163 0.55150 0 497 1876 26.492537 0.8354 1 511 3661 13.95794 0.5334 0 246 6.60580 0.1481 0 1256 33.72718 0.9305 1 496 2522 19.66693 0.7587 1 274 838 32.696897 0.8779 1 32 3479 0.9198045 0.42810 0 2606 3724 69.978518 0.81840 1 1440 21 1.4583333 0.3683 0 321 22.2916667 0.9036 1 97 1147 8.456844 0.8956 1 167 1147 14.5597210 0.82090 1 0 3724 0.0000 0.3640 0 3.55130 0.7859 2 3.14330 0.8145 3 0.81840 0.8108 1 3.35240 0.8725 3 10.86540 0.8550 9 4115 1534 1268 1676 3997 41.931449 0.8814 1 294 1809 16.252073 0.96740 1 341 814 41.891892 0.94320 1 204 454 44.93392 0.5438 0 545 1268 42.98107 0.83620 1 624 2425 25.731959 0.9002 1 994 4115 24.155529 0.9602 1 642 15.601458 0.4841 0 1126 27.36331 0.81750 1 568 2989.000 19.003011 0.7045 0 212 715.0000 29.6503497 0.85920 1 56 3825 1.4640523 0.53120 0 2715 4115.000 65.9781288 0.773200 1 1534 0 0.0000000 0.1079 0 529 34.4850065 0.9685 1 101 1268 7.9652997 0.87950 1 89 1268.000 7.018927 0.6184 0 17 4115 0.4131227 0.5707 0 4.54540 0.9754 5 3.39650 0.90810 2 0.773200 0.766700 1 3.14500 0.7858 2 11.860100 0.9520 10 Yes 0 0 $0 1 8000000 $8,000,000 1 Census Tract 106, Baldwin County, Alabama 17788 81600 16453 104700 20634.08 94656 -4181.08 -0.2026298 10044 0.1061105 NA NA Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003011000 01003 011000 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3758 2012 1576 1053 3758 28.02022 0.6597 0 66 1707 3.866432 0.16250 0 293 1297 22.59059 0.25080 0 83 279 29.74910 0.19030 0 376 1576 23.85787 0.15710 0 744 2723 27.322806 0.8465 1 996 4137 24.07542 0.8462 1 713 18.97286 0.8429 1 804 21.39436 0.3306 0 763 3295 23.15630 0.8670 1 155 1145 13.537118 0.4538 0 50 3475 1.4388489 0.51460 0 516 3758 13.730708 0.33300 0 2012 0 0.0000000 0.1224 0 606 30.1192843 0.9484 1 42 1576 2.664975 0.6476 0 96 1576 6.0913706 0.55620 0 0 3758 0.0000 0.3640 0 2.67200 0.5579 2 3.00890 0.7581 2 0.33300 0.3299 0 2.63860 0.5614 1 8.65250 0.6030 5 4921 1979 1732 1539 4908 31.356968 0.7523 1 150 2105 7.125891 0.72850 0 214 1471 14.547927 0.20260 0 59 261 22.60536 0.1167 0 273 1732 15.76212 0.07981 0 936 3332 28.091237 0.9206 1 861 4921 17.496444 0.8930 1 1039 21.113595 0.7653 1 1183 24.03983 0.64410 0 585 3738.000 15.650080 0.5371 0 81 1151.0000 7.0373588 0.19000 0 101 4546 2.2217334 0.61440 0 1244 4921.000 25.2794148 0.427800 0 1979 0 0.0000000 0.1079 0 527 26.6296109 0.9393 1 83 1732 4.7921478 0.77460 1 151 1732.000 8.718245 0.6904 0 20 4921 0.4064215 0.5688 0 3.37421 0.7528 3 2.75090 0.63780 1 0.427800 0.424200 0 3.08100 0.7597 2 9.633910 0.7366 6 Yes 0 0 $0 0 0 $0 0 Census Tract 110, Baldwin County, Alabama 19340 126400 23679 158700 22434.40 146624 1244.60 0.0554773 12076 0.0823603 129.69 188.85 Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003011406 01003 011406 AL Alabama Baldwin County 3 South Region 6 East South Central Division 3317 6418 1307 583 3317 17.57612 0.4181 0 70 1789 3.912800 0.16690 0 221 685 32.26277 0.57540 0 284 622 45.65916 0.52130 0 505 1307 38.63810 0.60430 0 168 2255 7.450111 0.2800 0 919 3677 24.99320 0.8623 1 452 13.62677 0.5791 0 673 20.28942 0.2668 0 366 2769 13.21777 0.4276 0 96 887 10.822999 0.3359 0 180 3066 5.8708415 0.77920 1 473 3317 14.259873 0.34330 0 6418 3976 61.9507635 0.9655 1 384 5.9831723 0.7063 0 17 1307 1.300689 0.4632 0 10 1307 0.7651109 0.08684 0 0 3317 0.0000 0.3640 0 2.33160 0.4577 1 2.38860 0.4323 1 0.34330 0.3401 0 2.58584 0.5335 1 7.64934 0.4576 3 3226 7850 1797 228 3215 7.091757 0.1241 0 72 2055 3.503650 0.33910 0 302 1139 26.514486 0.69300 0 230 658 34.95441 0.3131 0 532 1797 29.60490 0.52020 0 128 2726 4.695525 0.2384 0 530 3226 16.429014 0.8749 1 790 24.488531 0.8715 1 342 10.60136 0.05624 0 280 2884.000 9.708738 0.1832 0 58 792.0000 7.3232323 0.20270 0 15 3107 0.4827808 0.34070 0 15 3226.000 0.4649721 0.002512 0 7850 5394 68.7133758 0.9706 1 274 3.4904459 0.6697 0 23 1797 1.2799110 0.41980 0 26 1797.000 1.446856 0.1647 0 0 3226 0.0000000 0.1831 0 2.09670 0.3785 1 1.65434 0.08785 1 0.002512 0.002491 0 2.40790 0.4381 1 6.161452 0.2215 3 Yes 0 0 $0 0 0 $0 0 Census Tract 114.06, Baldwin County, Alabama 29838 252000 32201 224200 34612.08 292320 -2411.08 -0.0696601 -68120 -0.2330323 NA NA Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003011407 01003 011407 AL Alabama Baldwin County 3 South Region 6 East South Central Division 5187 6687 2066 1404 5172 27.14617 0.6423 0 172 1935 8.888889 0.63280 0 482 1433 33.63573 0.61530 0 367 633 57.97788 0.79510 1 849 2066 41.09390 0.67110 0 278 3618 7.683803 0.2906 0 1027 4945 20.76845 0.7735 1 1398 26.95200 0.9629 1 1263 24.34933 0.5302 0 596 3792 15.71730 0.5759 0 158 1633 9.675444 0.2833 0 29 4867 0.5958496 0.35240 0 170 5187 3.277424 0.07984 0 6687 2772 41.4535666 0.9251 1 197 2.9460147 0.6326 0 90 2066 4.356244 0.7729 1 0 2066 0.0000000 0.02586 0 0 5187 0.0000 0.3640 0 3.01030 0.6516 1 2.70470 0.6077 1 0.07984 0.0791 0 2.72046 0.6014 2 8.51530 0.5852 4 5608 7576 2543 1058 5602 18.886112 0.4835 0 32 2631 1.216268 0.05882 0 581 1979 29.358262 0.77080 1 309 564 54.78723 0.7671 1 890 2543 34.99803 0.67250 0 230 4433 5.188360 0.2698 0 776 5602 13.852196 0.8156 1 1527 27.228959 0.9205 1 567 10.11056 0.05099 0 615 5035.000 12.214498 0.3295 0 16 1746.0000 0.9163803 0.01566 0 0 5573 0.0000000 0.09479 0 441 5608.000 7.8637660 0.140300 0 7576 3055 40.3247096 0.9148 1 72 0.9503696 0.5383 0 0 2543 0.0000000 0.09796 0 125 2543.000 4.915454 0.4934 0 6 5608 0.1069900 0.4054 0 2.30022 0.4418 1 1.41144 0.04295 1 0.140300 0.139100 0 2.44986 0.4589 1 6.301820 0.2416 3 Yes 0 0 $0 0 0 $0 0 Census Tract 114.07, Baldwin County, Alabama 22317 292600 28418 241100 25887.72 339416 2530.28 0.0977406 -98316 -0.2896622 NA NA Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380
01003011502 01003 011502 AL Alabama Baldwin County 3 South Region 6 East South Central Division 9234 4606 3702 3160 9213 34.29936 0.7632 1 282 4002 7.046477 0.47570 0 526 2158 24.37442 0.31260 0 582 1544 37.69430 0.33410 0 1108 3702 29.92977 0.33740 0 997 6176 16.143135 0.6201 0 2074 10111 20.51231 0.7670 1 1450 15.70284 0.7043 0 2491 26.97639 0.6984 0 1542 7577 20.35106 0.7842 1 684 2718 25.165563 0.7767 1 532 8697 6.1170519 0.78590 1 3275 9234 35.466753 0.60970 0 4606 214 4.6461138 0.5268 0 828 17.9765523 0.8689 1 89 3702 2.404106 0.6192 0 293 3702 7.9146407 0.64700 0 0 9234 0.0000 0.3640 0 2.96340 0.6387 2 3.74950 0.9623 3 0.60970 0.6040 0 3.02590 0.7475 1 10.34850 0.8024 6 14165 6867 6002 2853 14165 20.141193 0.5175 0 313 7047 4.441606 0.46620 0 1181 4164 28.362152 0.74500 0 887 1838 48.25898 0.6211 0 2068 6002 34.45518 0.65900 0 1667 10750 15.506977 0.7286 0 2527 14165 17.839746 0.8980 1 3082 21.757854 0.7907 1 2506 17.69149 0.24240 0 3004 11659.000 25.765503 0.9038 1 407 3482.0000 11.6886847 0.39940 0 364 13519 2.6925068 0.65290 0 2755 14165.000 19.4493470 0.346300 0 6867 441 6.4220183 0.5555 0 526 7.6598223 0.7585 1 93 6002 1.5494835 0.46540 0 184 6002.000 3.065645 0.3373 0 0 14165 0.0000000 0.1831 0 3.26930 0.7261 1 2.98920 0.76250 2 0.346300 0.343400 0 2.29980 0.3856 1 8.904600 0.6398 4 Yes 0 0 $0 2 8860000 $8,860,000 1 Census Tract 115.02, Baldwin County, Alabama 20411 162700 22820 180400 23676.76 188732 -856.76 -0.0361857 -8332 -0.0441473 NA NA Baldwin County, Alabama Mobile-Daphne-Fairhope, AL CSA CS380

LIHTC Data

Divisional

svi_divisional_lihtc_df0 <- left_join(svi_divisional_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_divisional_lihtc_df1 <- left_join(svi_divisional_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_divisional_lihtc_df <- left_join(svi_divisional_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_divisional_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt county_fips FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility NAME Median_Income_10 Median_Home_Value_10 Median_Income_19 Median_Home_Value_19 Median_Income_10adj Median_Home_Value_10adj Median_Income_Change Median_Income_Change_pct Median_Home_Value_Change Median_Home_Value_Change_pct housing_price_index10 housing_price_index20 county_title cbsa cbsa_code
04001942600 04001 942600 AZ Arizona Apache County 4 West Region 8 Mountain Division 1561 762 384 1150 1561 73.67072 0.9944 1 26 300 8.666667 0.6866 0 65 366 17.759563 0.10180 0 5 18 27.77778 0.19090 0 70 384 18.22917 0.05781 0 303 839 36.11442 0.9335 1 282 1578 17.87072 0.5921 0 153 9.801409 0.449600 0 560 35.874440 0.90440 1 240 1054 22.770398 0.90060 1 107 332 32.22892 0.9163 1 168 1431 11.7400419 0.8831 1 1561 1561 100.00000 0.9989 1 762 0 0.0000000 0.1526 0 215 28.21522 0.9088 1 117 384 30.468750 0.9979 1 33 384 8.593750 0.7842 1 0 1561 0.000000 0.3955 0 3.26441 0.7248 2 4.054000 0.98530 4 0.9989 0.9931 1 3.2390 0.8004 3 11.556310 0.8966 10 1711 676 469 930 1711 54.35418 0.9708 1 44 484 9.090909 0.8539 1 32 456 7.017544 0.02013 0 4 13 30.76923 0.24630 0 36 469 7.675906 0.005758 0 304 1197 25.396825 0.9056 1 686 1711 40.093513 0.9973 1 229 13.3839860 0.439700 0 347 20.280538 0.37880 0 245 1363.979 17.962156 0.6824 0 49 304.0000 16.118421 0.5859 0 155 1652 9.3825666 0.8951 1 1711 1710.980 100.00115 1.0000 1 676 0 0.0000000 0.1276 0 142 21.0059172 0.8736 1 83 469 17.697228 0.9774 1 99 469.0000 21.108742 0.96550 1 0 1711 0.0000000 0.2155 0 3.733358 0.8474 4 2.981900 0.73750 1 1.0000 0.9958 1 3.15960 0.7653 3 10.87486 0.8573 9 0 0 0 0 0 Yes Census Tract 9426, Apache County, Arizona 10268 27600 15822 45700 11910.88 32016 3911.12 0.3283653 13684 0.4274113 NA NA NA NA NA
04001942700 04001 942700 AZ Arizona Apache County 4 West Region 8 Mountain Division 4886 2757 1291 2616 4871 53.70560 0.9480 1 163 1398 11.659514 0.8577 1 102 1113 9.164421 0.01757 0 54 178 30.33708 0.22790 0 156 1291 12.08366 0.01652 0 1039 2931 35.44865 0.9303 1 1873 5249 35.68299 0.9436 1 688 14.081048 0.687000 0 1530 31.313958 0.77180 1 772 3514 21.969266 0.88390 1 246 939 26.19808 0.8308 1 592 4631 12.7834161 0.8975 1 4846 4886 99.18133 0.9946 1 2757 0 0.0000000 0.1526 0 369 13.38411 0.7652 1 240 1291 18.590240 0.9756 1 188 1291 14.562355 0.9015 1 0 4886 0.000000 0.3955 0 3.69612 0.8288 4 4.071000 0.98700 4 0.9946 0.9890 1 3.1904 0.7848 3 11.952120 0.9295 12 5469 2222 1462 2784 5469 50.90510 0.9557 1 358 1642 21.802680 0.9925 1 114 1151 9.904431 0.04797 0 58 311 18.64952 0.09477 0 172 1462 11.764706 0.023990 0 852 3274 26.023213 0.9120 1 1856 5466 33.955360 0.9919 1 759 13.8782227 0.465700 0 1555 28.432986 0.77390 1 706 3911.002 18.051640 0.6872 0 257 1035.0004 24.830908 0.8039 1 396 5078 7.7983458 0.8624 1 5420 5469.002 99.10401 0.9946 1 2222 0 0.0000000 0.1276 0 400 18.0018002 0.8488 1 238 1462 16.279070 0.9710 1 175 1462.0007 11.969898 0.87420 1 26 5469 0.4754068 0.6430 0 3.876090 0.8796 4 3.593100 0.94210 3 0.9946 0.9905 1 3.46460 0.8721 3 11.92839 0.9425 11 0 0 0 0 0 Yes Census Tract 9427, Apache County, Arizona 14348 55900 18740 47200 16643.68 64844 2096.32 0.1259529 -17644 -0.2720992 NA NA NA NA NA
04001944100 04001 944100 AZ Arizona Apache County 4 West Region 8 Mountain Division 4975 2485 1204 3251 4968 65.43881 0.9846 1 210 1254 16.746412 0.9576 1 122 905 13.480663 0.04383 0 91 299 30.43478 0.22960 0 213 1204 17.69103 0.05320 0 779 2325 33.50538 0.9203 1 1293 5511 23.46217 0.7705 1 344 6.914573 0.270100 0 1993 40.060302 0.97010 1 577 3087 18.691286 0.77990 1 278 893 31.13102 0.9038 1 308 4470 6.8903803 0.7895 1 4915 4975 98.79397 0.9929 1 2485 21 0.8450704 0.3700 0 428 17.22334 0.8203 1 257 1204 21.345515 0.9843 1 212 1204 17.607973 0.9391 1 0 4975 0.000000 0.3955 0 3.68620 0.8261 4 3.713400 0.95280 4 0.9929 0.9872 1 3.5092 0.8926 3 11.901700 0.9244 12 6183 2379 1424 3704 5789 63.98342 0.9912 1 425 1608 26.430348 0.9954 1 132 1163 11.349957 0.07802 0 38 261 14.55939 0.06498 0 170 1424 11.938202 0.026300 0 862 3259 26.449831 0.9148 1 1320 6183 21.348860 0.9283 1 637 10.3024422 0.271800 0 1869 30.228045 0.83960 1 626 3964.000 15.792129 0.5715 0 371 991.0000 37.436932 0.9557 1 315 5717 5.5098828 0.8021 1 5981 6182.998 96.73300 0.9841 1 2379 0 0.0000000 0.1276 0 442 18.5792350 0.8550 1 379 1424 26.615168 0.9969 1 347 1424.0000 24.367977 0.97580 1 394 6183 6.3723112 0.9380 1 3.856000 0.8749 4 3.440700 0.90700 3 0.9841 0.9800 1 3.89330 0.9609 4 12.17410 0.9549 12 0 0 0 0 0 Yes Census Tract 9441, Apache County, Arizona 13469 60900 16162 46800 15624.04 70644 537.96 0.0344316 -23844 -0.3375234 NA NA NA NA NA
04001944300 04001 944300 AZ Arizona Apache County 4 West Region 8 Mountain Division 6806 3308 1826 4099 6797 60.30602 0.9762 1 403 1777 22.678672 0.9858 1 154 1457 10.569664 0.02549 0 63 369 17.07317 0.08684 0 217 1826 11.88390 0.01536 0 1432 3367 42.53044 0.9623 1 2305 7092 32.50141 0.9160 1 746 10.960917 0.517600 0 2767 40.655304 0.97610 1 842 4361 19.307498 0.80410 1 357 1163 30.69647 0.8982 1 568 6178 9.1939139 0.8423 1 6750 6806 99.17720 0.9944 1 3308 8 0.2418380 0.3113 0 440 13.30109 0.7638 1 404 1826 22.124863 0.9856 1 388 1826 21.248631 0.9627 1 139 6806 2.042316 0.8458 1 3.85566 0.8602 4 4.038300 0.98440 4 0.9944 0.9888 1 3.8692 0.9619 4 12.757560 0.9749 13 5922 2801 2026 3548 5916 59.97295 0.9854 1 67 1402 4.778887 0.5316 0 251 1664 15.084135 0.20570 0 46 362 12.70718 0.05498 0 297 2026 14.659427 0.056430 0 844 3696 22.835498 0.8792 1 2528 5916 42.731575 0.9987 1 793 13.3907464 0.440100 0 1663 28.081729 0.75750 1 573 4258.743 13.454674 0.4253 0 301 1112.2581 27.062064 0.8474 1 851 5568 15.2837644 0.9575 1 5880 5922.449 99.28326 0.9964 1 2801 22 0.7854338 0.3369 0 521 18.6004998 0.8557 1 267 2026 13.178677 0.9482 1 297 2025.6898 14.661672 0.91580 1 11 5922 0.1857481 0.5222 0 3.451330 0.7773 3 3.427800 0.90080 3 0.9964 0.9922 1 3.57880 0.9040 3 11.45433 0.9088 10 0 0 0 0 0 Yes Census Tract 9443, Apache County, Arizona 11133 48700 15051 53700 12914.28 56492 2136.72 0.1654541 -2792 -0.0494229 NA NA NA NA NA
04005000800 04005 000800 AZ Arizona Coconino County 4 West Region 8 Mountain Division 3912 1200 1057 1511 2859 52.85065 0.9430 1 54 1952 2.766393 0.1150 0 71 192 36.979167 0.73370 0 509 865 58.84393 0.83080 1 580 1057 54.87228 0.96160 1 265 1897 13.96943 0.6489 0 995 3589 27.72360 0.8536 1 121 3.093047 0.062070 0 208 5.316973 0.02835 0 248 3170 7.823344 0.15510 0 53 311 17.04180 0.5919 0 26 3898 0.6670087 0.3063 0 1410 3912 36.04294 0.6285 0 1200 155 12.9166667 0.7329 0 3 0.25000 0.3706 0 31 1057 2.932829 0.6261 0 33 1057 3.122044 0.4682 0 1043 3912 26.661554 0.9826 1 3.52210 0.7887 3 1.143720 0.02019 0 0.6285 0.6250 0 3.1804 0.7810 1 8.474720 0.5850 4 6428 2343 2163 3238 5850 55.35043 0.9741 1 399 3753 10.631495 0.9047 1 43 312 13.782051 0.15050 0 1188 1850 64.21622 0.93540 1 1231 2162 56.938020 0.988900 1 364 2823 12.894084 0.7116 0 478 5900 8.101695 0.4937 0 262 4.0759179 0.030250 0 634 9.863099 0.06202 0 497 5227.333 9.507716 0.1782 0 112 544.6422 20.563958 0.7139 0 56 6207 0.9022072 0.4074 0 2862 6428.175 44.52274 0.6490 0 2343 838 35.7661118 0.9165 1 11 0.4694836 0.4053 0 116 2163 5.362922 0.7808 1 166 2162.6681 7.675704 0.76580 1 759 6428 11.8077162 0.9625 1 4.073000 0.9190 3 1.391770 0.04857 0 0.6490 0.6463 0 3.83090 0.9524 4 9.94467 0.7611 7 0 0 0 0 0 Yes Census Tract 8, Coconino County, Arizona 8201 236500 14117 292500 9513.16 274340 4603.84 0.4839443 18160 0.0661952 151.60 249.17 Coconino County, Arizona Flagstaff, AZ MSA C2238
04005001000 04005 001000 AZ Arizona Coconino County 4 West Region 8 Mountain Division 7519 863 763 1197 1744 68.63532 0.9894 1 1067 4202 25.392670 0.9925 1 17 25 68.000000 0.99610 1 484 738 65.58266 0.91810 1 501 763 65.66186 0.99650 1 47 886 5.30474 0.2676 0 1429 8331 17.15280 0.5641 0 0 0.000000 0.003736 0 310 4.122889 0.02165 0 54 1560 3.461539 0.01727 0 23 174 13.21839 0.4495 0 233 7411 3.1439752 0.6314 0 2495 7519 33.18260 0.5941 0 863 441 51.1008111 0.9666 1 35 4.05562 0.6079 0 14 763 1.834862 0.4856 0 119 763 15.596330 0.9127 1 5775 7519 76.805426 0.9946 1 3.81010 0.8520 3 1.123556 0.01848 0 0.5941 0.5907 0 3.9674 0.9733 3 9.495156 0.7036 6 13499 815 675 1056 1313 80.42650 0.9987 1 1353 6344 21.327238 0.9918 1 22 35 62.857143 0.99810 1 500 641 78.00312 0.99310 1 522 676 77.218935 0.999600 1 29 460 6.304348 0.4346 0 1051 13483 7.795001 0.4716 0 17 0.1259353 0.004211 0 221 1.637158 0.01474 0 125 1282.667 9.745322 0.1874 0 42 114.3578 36.726848 0.9505 1 207 13491 1.5343562 0.5203 0 4803 13498.825 35.58088 0.5539 0 815 550 67.4846626 0.9864 1 7 0.8588957 0.4653 0 62 675 9.185185 0.8933 1 134 675.3319 19.842095 0.96070 1 12185 13499 90.2659456 0.9960 1 3.896300 0.8850 3 1.677151 0.11070 1 0.5539 0.5516 0 4.30170 0.9882 4 10.42905 0.8107 8 0 0 0 0 0 Yes Census Tract 10, Coconino County, Arizona 4710 118400 4039 NA 5463.60 137344 -1424.60 -0.2607438 NA NA NA NA Coconino County, Arizona Flagstaff, AZ MSA C2238
04007940200 04007 940200 AZ Arizona Gila County 4 West Region 8 Mountain Division 760 380 198 599 760 78.81579 0.9971 1 14 202 6.930693 0.5411 0 56 130 43.076923 0.87120 1 12 68 17.64706 0.09167 0 68 198 34.34343 0.48740 0 142 386 36.78756 0.9383 1 494 1394 35.43759 0.9419 1 39 5.131579 0.165100 0 294 38.684210 0.95480 1 245 807 30.359356 0.98120 1 33 141 23.40426 0.7730 1 36 676 5.3254438 0.7379 0 760 760 100.00000 0.9989 1 380 0 0.0000000 0.1526 0 55 14.47368 0.7834 1 12 198 6.060606 0.8228 1 31 198 15.656566 0.9136 1 0 760 0.000000 0.3955 0 3.90580 0.8699 3 3.612000 0.93710 3 0.9989 0.9931 1 3.0679 0.7370 3 11.584600 0.8992 10 2341 555 478 1464 2332 62.77873 0.9891 1 103 582 17.697595 0.9847 1 28 301 9.302326 0.03891 0 31 177 17.51412 0.08439 0 59 478 12.343096 0.031290 0 364 1073 33.923579 0.9585 1 300 2341 12.815036 0.7377 0 149 6.3648014 0.087670 0 1122 47.928236 0.99900 1 303 1219.000 24.856440 0.9002 1 133 395.0000 33.670886 0.9294 1 27 2020 1.3366337 0.4895 0 2321 2341.000 99.14566 0.9948 1 555 0 0.0000000 0.1276 0 22 3.9639640 0.6253 0 137 478 28.661088 0.9977 1 102 478.0000 21.338912 0.96700 1 0 2341 0.0000000 0.2155 0 3.701290 0.8371 3 3.405770 0.89580 3 0.9948 0.9907 1 2.93310 0.6760 2 11.03496 0.8720 9 0 0 0 0 0 Yes Census Tract 9402, Gila County, Arizona 20607 83800 14301 55800 23904.12 97208 -9603.12 -0.4017349 -41408 -0.4259732 NA NA Gila County, Arizona Payson, AZ MicroSA C3774
04007940400 04007 940400 AZ Arizona Gila County 4 West Region 8 Mountain Division 6485 1464 1041 3673 6485 56.63840 0.9620 1 277 1834 15.103599 0.9368 1 101 681 14.831131 0.05870 0 56 360 15.55556 0.07525 0 157 1041 15.08165 0.03361 0 1155 3309 34.90481 0.9266 1 2644 5767 45.84706 0.9889 1 408 6.291442 0.234900 0 2406 37.101002 0.93050 1 692 3817 18.129421 0.76010 1 354 861 41.11498 0.9756 1 30 5565 0.5390836 0.2729 0 6428 6485 99.12105 0.9937 1 1464 0 0.0000000 0.1526 0 335 22.88251 0.8750 1 248 1041 23.823247 0.9891 1 202 1041 19.404419 0.9537 1 0 6485 0.000000 0.3955 0 3.84791 0.8589 4 3.174000 0.80420 3 0.9937 0.9880 1 3.3659 0.8488 3 11.381510 0.8800 11 5873 1751 1496 3435 5862 58.59775 0.9829 1 357 1977 18.057663 0.9860 1 114 799 14.267835 0.17090 0 114 697 16.35581 0.07632 0 228 1496 15.240642 0.066600 0 819 3169 25.844115 0.9099 1 486 5872 8.276567 0.5054 0 464 7.9005619 0.154300 0 2015 34.309552 0.94120 1 697 3857.000 18.071040 0.6887 0 399 1240.0000 32.177419 0.9121 1 116 5320 2.1804511 0.6047 0 5766 5873.000 98.17810 0.9899 1 1751 10 0.5711022 0.3116 0 410 23.4151913 0.8922 1 290 1496 19.385027 0.9818 1 322 1496.0000 21.524064 0.96800 1 4 5873 0.0681083 0.4458 0 3.450800 0.7771 3 3.301000 0.86590 2 0.9899 0.9857 1 3.59940 0.9095 3 11.34110 0.8981 9 0 0 1 547218 1 Yes Census Tract 9404, Gila County, Arizona 14125 57900 19488 40200 16385.00 67164 3103.00 0.1893805 -26964 -0.4014651 NA NA Gila County, Arizona Payson, AZ MicroSA C3774
04009940500 04009 940500 AZ Arizona Graham County 4 West Region 8 Mountain Division 4838 1186 1054 3261 4811 67.78217 0.9883 1 392 1501 26.115923 0.9935 1 118 582 20.274914 0.15470 0 86 472 18.22034 0.09660 0 204 1054 19.35484 0.07125 0 622 2290 27.16157 0.8736 1 2211 4615 47.90899 0.9927 1 235 4.857379 0.147100 0 2057 42.517569 0.98700 1 599 2750 21.781818 0.87800 1 453 874 51.83066 0.9942 1 134 4253 3.1507171 0.6320 0 4838 4838 100.00000 0.9989 1 1186 0 0.0000000 0.1526 0 157 13.23777 0.7625 1 259 1054 24.573055 0.9906 1 256 1054 24.288425 0.9752 1 0 4838 0.000000 0.3955 0 3.91935 0.8730 4 3.638300 0.94100 3 0.9989 0.9931 1 3.2764 0.8163 3 11.832950 0.9185 11 4698 1271 1088 2479 4644 53.38071 0.9680 1 320 1512 21.164021 0.9916 1 49 635 7.716535 0.02543 0 59 453 13.02428 0.05556 0 108 1088 9.926471 0.011900 0 524 2457 21.326821 0.8647 1 748 4698 15.921669 0.8382 1 411 8.7484036 0.196800 0 1750 37.249894 0.97360 1 483 2948.001 16.383982 0.6023 0 243 867.0002 28.027676 0.8613 1 28 4096 0.6835938 0.3526 0 4691 4698.001 99.85097 0.9987 1 1271 0 0.0000000 0.1276 0 132 10.3855232 0.7441 0 187 1088 17.187500 0.9751 1 190 1088.0003 17.463231 0.94690 1 8 4698 0.1702852 0.5117 0 3.674400 0.8291 4 2.986600 0.73960 2 0.9987 0.9945 1 3.30540 0.8183 2 10.96510 0.8644 9 0 0 0 0 0 Yes Census Tract 9405, Graham County, Arizona 12156 52100 17406 30200 14100.96 60436 3305.04 0.2343840 -30236 -0.5002978 NA NA Graham County, Arizona Safford, AZ MicroSA C4094
04013050603 04013 050603 AZ Arizona Maricopa County 4 West Region 8 Mountain Division 3210 1306 1019 935 3203 29.19138 0.6976 0 123 1440 8.541667 0.6788 0 173 827 20.918984 0.17220 0 80 192 41.66667 0.44980 0 253 1019 24.82826 0.17710 0 478 1973 24.22707 0.8412 1 973 3641 26.72343 0.8371 1 284 8.847352 0.386600 0 996 31.028037 0.76090 1 303 2615 11.586998 0.38900 0 88 810 10.86420 0.3488 0 138 3010 4.5847176 0.7109 0 1310 3210 40.80997 0.6759 0 1306 0 0.0000000 0.1526 0 720 55.13017 0.9795 1 69 1019 6.771344 0.8464 1 39 1019 3.827282 0.5281 0 0 3210 0.000000 0.3955 0 3.23180 0.7166 2 2.596200 0.55280 1 0.6759 0.6720 0 2.9021 0.6670 2 9.406000 0.6968 5 6148 2245 1921 1186 6098 19.44900 0.5140 0 378 2678 14.115011 0.9622 1 277 1535 18.045603 0.35540 0 43 387 11.11111 0.04642 0 320 1922 16.649324 0.093090 0 1099 4207 26.123128 0.9131 1 1084 6144 17.643229 0.8762 1 814 13.2400781 0.433200 0 1313 21.356539 0.43200 0 1259 4836.918 26.028972 0.9197 1 72 1493.4212 4.821145 0.1184 0 372 5967 6.2342886 0.8243 1 2088 6148.289 33.96067 0.5342 0 2245 0 0.0000000 0.1276 0 1081 48.1514477 0.9699 1 65 1921 3.383654 0.6475 0 0 1921.1833 0.000000 0.03808 0 7 6148 0.1138582 0.4734 0 3.358590 0.7560 3 2.727600 0.61500 2 0.5342 0.5320 0 2.25648 0.3825 1 8.87687 0.6322 6 0 0 0 0 0 Yes Census Tract 506.03, Maricopa County, Arizona 26439 156000 23698 165600 30669.24 180960 -6971.24 -0.2273040 -15360 -0.0848806 83.88 195.56 Maricopa County, Arizona Phoenix-Mesa-Scottsdale, AZ MSA C3806

National

svi_national_lihtc_df0 <- left_join(svi_national_lihtc, census_pull_df, join_by("GEOID_2010_trt" == "GEOID"))

svi_national_lihtc_df1 <- left_join(svi_national_lihtc_df0, hpi_df_10_20, join_by("GEOID_2010_trt" == "GEOID10")) %>%
                          unite("county_fips", FIPS_st, FIPS_county, sep = "") 

svi_national_lihtc_df <- left_join(svi_national_lihtc_df1, msa_csa_crosswalk, join_by("county_fips" == "county_fips"))

svi_national_lihtc_df %>% head(10) %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt county_fips FIPS_tract state state_name county region_number region division_number division E_TOTPOP_10 E_HU_10 E_HH_10 E_POV150_10 ET_POVSTATUS_10 EP_POV150_10 EPL_POV150_10 F_POV150_10 E_UNEMP_10 ET_EMPSTATUS_10 EP_UNEMP_10 EPL_UNEMP_10 F_UNEMP_10 E_HBURD_OWN_10 ET_HOUSINGCOST_OWN_10 EP_HBURD_OWN_10 EPL_HBURD_OWN_10 F_HBURD_OWN_10 E_HBURD_RENT_10 ET_HOUSINGCOST_RENT_10 EP_HBURD_RENT_10 EPL_HBURD_RENT_10 F_HBURD_RENT_10 E_HBURD_10 ET_HOUSINGCOST_10 EP_HBURD_10 EPL_HBURD_10 F_HBURD_10 E_NOHSDP_10 ET_EDSTATUS_10 EP_NOHSDP_10 EPL_NOHSDP_10 F_NOHSDP_10 E_UNINSUR_12 ET_INSURSTATUS_12 EP_UNINSUR_12 EPL_UNINSUR_12 F_UNINSUR_12 E_AGE65_10 EP_AGE65_10 EPL_AGE65_10 F_AGE65_10 E_AGE17_10 EP_AGE17_10 EPL_AGE17_10 F_AGE17_10 E_DISABL_12 ET_DISABLSTATUS_12 EP_DISABL_12 EPL_DISABL_12 F_DISABL_12 E_SNGPNT_10 ET_FAMILIES_10 EP_SNGPNT_10 EPL_SNGPNT_10 F_SNGPNT_10 E_LIMENG_10 ET_POPAGE5UP_10 EP_LIMENG_10 EPL_LIMENG_10 F_LIMENG_10 E_MINRTY_10 ET_POPETHRACE_10 EP_MINRTY_10 EPL_MINRTY_10 F_MINRTY_10 E_STRHU_10 E_MUNIT_10 EP_MUNIT_10 EPL_MUNIT_10 F_MUNIT_10 E_MOBILE_10 EP_MOBILE_10 EPL_MOBILE_10 F_MOBILE_10 E_CROWD_10 ET_OCCUPANTS_10 EP_CROWD_10 EPL_CROWD_10 F_CROWD_10 E_NOVEH_10 ET_KNOWNVEH_10 EP_NOVEH_10 EPL_NOVEH_10 F_NOVEH_10 E_GROUPQ_10 ET_HHTYPE_10 EP_GROUPQ_10 EPL_GROUPQ_10 F_GROUPQ_10 SPL_THEME1_10 RPL_THEME1_10 F_THEME1_10 SPL_THEME2_10 RPL_THEME2_10 F_THEME2_10 SPL_THEME3_10 RPL_THEME3_10 F_THEME3_10 SPL_THEME4_10 RPL_THEME4_10 F_THEME4_10 SPL_THEMES_10 RPL_THEMES_10 F_TOTAL_10 E_TOTPOP_20 E_HU_20 E_HH_20 E_POV150_20 ET_POVSTATUS_20 EP_POV150_20 EPL_POV150_20 F_POV150_20 E_UNEMP_20 ET_EMPSTATUS_20 EP_UNEMP_20 EPL_UNEMP_20 F_UNEMP_20 E_HBURD_OWN_20 ET_HOUSINGCOST_OWN_20 EP_HBURD_OWN_20 EPL_HBURD_OWN_20 F_HBURD_OWN_20 E_HBURD_RENT_20 ET_HOUSINGCOST_RENT_20 EP_HBURD_RENT_20 EPL_HBURD_RENT_20 F_HBURD_RENT_20 E_HBURD_20 ET_HOUSINGCOST_20 EP_HBURD_20 EPL_HBURD_20 F_HBURD_20 E_NOHSDP_20 ET_EDSTATUS_20 EP_NOHSDP_20 EPL_NOHSDP_20 F_NOHSDP_20 E_UNINSUR_20 ET_INSURSTATUS_20 EP_UNINSUR_20 EPL_UNINSUR_20 F_UNINSUR_20 E_AGE65_20 EP_AGE65_20 EPL_AGE65_20 F_AGE65_20 E_AGE17_20 EP_AGE17_20 EPL_AGE17_20 F_AGE17_20 E_DISABL_20 ET_DISABLSTATUS_20 EP_DISABL_20 EPL_DISABL_20 F_DISABL_20 E_SNGPNT_20 ET_FAMILIES_20 EP_SNGPNT_20 EPL_SNGPNT_20 F_SNGPNT_20 E_LIMENG_20 ET_POPAGE5UP_20 EP_LIMENG_20 EPL_LIMENG_20 F_LIMENG_20 E_MINRTY_20 ET_POPETHRACE_20 EP_MINRTY_20 EPL_MINRTY_20 F_MINRTY_20 E_STRHU_20 E_MUNIT_20 EP_MUNIT_20 EPL_MUNIT_20 F_MUNIT_20 E_MOBILE_20 EP_MOBILE_20 EPL_MOBILE_20 F_MOBILE_20 E_CROWD_20 ET_OCCUPANTS_20 EP_CROWD_20 EPL_CROWD_20 F_CROWD_20 E_NOVEH_20 ET_KNOWNVEH_20 EP_NOVEH_20 EPL_NOVEH_20 F_NOVEH_20 E_GROUPQ_20 ET_HHTYPE_20 EP_GROUPQ_20 EPL_GROUPQ_20 F_GROUPQ_20 SPL_THEME1_20 RPL_THEME1_20 F_THEME1_20 SPL_THEME2_20 RPL_THEME2_20 F_THEME2_20 SPL_THEME3_20 RPL_THEME3_20 F_THEME3_20 SPL_THEME4_20 RPL_THEME4_20 F_THEME4_20 SPL_THEMES_20 RPL_THEMES_20 F_TOTAL_20 pre10_lihtc_project_cnt pre10_lihtc_project_dollars post10_lihtc_project_cnt post10_lihtc_project_dollars lihtc_flag lihtc_eligibility NAME Median_Income_10 Median_Home_Value_10 Median_Income_19 Median_Home_Value_19 Median_Income_10adj Median_Home_Value_10adj Median_Income_Change Median_Income_Change_pct Median_Home_Value_Change Median_Home_Value_Change_pct housing_price_index10 housing_price_index20 county_title cbsa cbsa_code
01005950700 01005 950700 AL Alabama Barbour County 3 South Region 6 East South Central Division 1753 687 563 615 1628 37.77641 0.8088 1 17 667 2.548726 0.06941 0 41 376 10.90426 0.01945 0 62 187 33.15508 0.24640 0 103 563 18.29485 0.04875 0 264 1208 21.85430 0.7570 1 201 1527 13.163065 0.4991 0 368 20.992584 0.89510 1 462 26.354820 0.66130 0 211 1085 19.44700 0.7505 1 107 399 26.81704 0.8048 1 0 1628 0.0000000 0.09298 0 861 1753 49.11580 0.7101 0 687 17 2.4745269 0.4324 0 38 5.5312955 0.6970 0 3 563 0.5328597 0.3037 0 19 563 3.374778 0.3529 0 233 1753 13.29150 0.9517 1 2.18306 0.4137 2 3.20468 0.8377 3 0.7101 0.7035 0 2.7377 0.6100 1 8.83554 0.6264 6 1527 691 595 565 1365 41.39194 0.8765 1 37 572 6.468532 0.6776 0 70 376 18.617021 0.38590 0 92 219 42.009132 0.47360 0 162 595 27.22689 0.44540 0 280 1114 25.13465 0.8942 1 105 1378 7.619739 0.5505 0 383 25.081860 0.88450 1 337 22.069417 0.51380 0 237 1041.0000 22.76657 0.8360 1 144 413.0000 34.86683 0.9114 1 11 1466 0.7503411 0.40700 0 711 1527.0000 46.56189 0.6441 0 691 13 1.8813314 0.3740 0 37 5.3545586 0.7152 0 0 595 0.0000000 0.09796 0 115 595.0000 19.327731 0.8859 1 149 1527 9.7576948 0.9470 1 3.44420 0.7707 2 3.55270 0.9403 3 0.6441 0.6387 0 3.02006 0.7337 2 10.66106 0.8537 7 0 0 0 0 0 Yes Census Tract 9507, Barbour County, Alabama 15257 133700 17244 137500 17698.12 155092 -454.12 -0.0256592 -17592 -0.1134294 131.05 135.61 Barbour County, Alabama Eufaula, AL-GA MicroSA C2164
01011952100 01011 952100 AL Alabama Bullock County 3 South Region 6 East South Central Division 1652 796 554 564 1652 34.14044 0.7613 1 46 816 5.637255 0.33630 0 96 458 20.96070 0.19930 0 62 96 64.58333 0.89170 1 158 554 28.51986 0.29220 0 271 1076 25.18587 0.8163 1 155 1663 9.320505 0.3183 0 199 12.046005 0.47180 0 420 25.423729 0.60240 0 327 1279 25.56685 0.9151 1 137 375 36.53333 0.9108 1 0 1590 0.0000000 0.09298 0 1428 1652 86.44068 0.8939 1 796 0 0.0000000 0.1224 0 384 48.2412060 0.9897 1 19 554 3.4296029 0.7145 0 45 554 8.122744 0.6556 0 0 1652 0.00000 0.3640 0 2.52440 0.5138 2 2.99308 0.7515 2 0.8939 0.8856 1 2.8462 0.6637 1 9.25758 0.6790 6 1382 748 549 742 1382 53.69030 0.9560 1 40 511 7.827789 0.7730 1 110 402 27.363184 0.71780 0 45 147 30.612245 0.23070 0 155 549 28.23315 0.47730 0 181 905 20.00000 0.8253 1 232 1382 16.787265 0.8813 1 164 11.866860 0.27170 0 250 18.089725 0.26290 0 258 1132.0000 22.79152 0.8368 1 99 279.0000 35.48387 0.9162 1 33 1275 2.5882353 0.64520 0 1347 1382.0000 97.46744 0.9681 1 748 0 0.0000000 0.1079 0 375 50.1336898 0.9922 1 0 549 0.0000000 0.09796 0 37 549.0000 6.739526 0.6039 0 0 1382 0.0000000 0.1831 0 3.91290 0.8785 4 2.93280 0.7342 2 0.9681 0.9599 1 1.98506 0.2471 1 9.79886 0.7570 8 0 0 0 0 0 Yes Census Tract 9521, Bullock County, Alabama 19754 58200 18598 66900 22914.64 67512 -4316.64 -0.1883791 -612 -0.0090651 NA NA NA NA NA
01015000300 01015 000300 AL Alabama Calhoun County 3 South Region 6 East South Central Division 3074 1635 1330 1904 3067 62.08021 0.9710 1 293 1362 21.512482 0.96630 1 180 513 35.08772 0.65450 0 383 817 46.87882 0.55040 0 563 1330 42.33083 0.70280 0 720 2127 33.85049 0.9148 1 628 2835 22.151675 0.8076 1 380 12.361744 0.49340 0 713 23.194535 0.45030 0 647 2111 30.64898 0.9708 1 298 773 38.55110 0.9247 1 0 2878 0.0000000 0.09298 0 2623 3074 85.32856 0.8883 1 1635 148 9.0519878 0.6465 0 6 0.3669725 0.4502 0 68 1330 5.1127820 0.8082 1 303 1330 22.781955 0.9029 1 0 3074 0.00000 0.3640 0 4.36250 0.9430 4 2.93218 0.7233 2 0.8883 0.8800 1 3.1718 0.8070 2 11.35478 0.9009 9 2390 1702 1282 1287 2390 53.84937 0.9566 1 102 1066 9.568480 0.8541 1 158 609 25.944171 0.67520 0 286 673 42.496285 0.48560 0 444 1282 34.63339 0.66340 0 467 1685 27.71513 0.9180 1 369 2379 15.510719 0.8562 1 342 14.309623 0.40850 0 548 22.928870 0.57100 0 647 1831.0000 35.33588 0.9862 1 202 576.0000 35.06944 0.9130 1 16 2134 0.7497657 0.40690 0 1896 2390.0000 79.33054 0.8451 1 1702 96 5.6404230 0.5329 0 0 0.0000000 0.2186 0 0 1282 0.0000000 0.09796 0 186 1282.0000 14.508580 0.8308 1 43 2390 1.7991632 0.7727 1 4.24830 0.9395 4 3.28560 0.8773 2 0.8451 0.8379 1 2.45296 0.4602 2 10.83196 0.8718 9 0 0 0 0 0 Yes Census Tract 3, Calhoun County, Alabama 12211 41700 18299 51300 14164.76 48372 4134.24 0.2918680 2928 0.0605309 NA NA Calhoun County, Alabama Anniston-Oxford, AL MSA C1150
01015000500 01015 000500 AL Alabama Calhoun County 3 South Region 6 East South Central Division 1731 1175 743 1042 1619 64.36072 0.9767 1 124 472 26.271186 0.98460 1 136 461 29.50108 0.48970 0 163 282 57.80142 0.79190 1 299 743 40.24226 0.64910 0 340 1270 26.77165 0.8389 1 460 1794 25.641026 0.8722 1 271 15.655690 0.70190 0 368 21.259388 0.32190 0 507 1449 34.98965 0.9885 1 150 386 38.86010 0.9269 1 0 1677 0.0000000 0.09298 0 1559 1731 90.06355 0.9123 1 1175 50 4.2553191 0.5128 0 4 0.3404255 0.4480 0 0 743 0.0000000 0.1238 0 122 743 16.419919 0.8473 1 0 1731 0.00000 0.3640 0 4.32150 0.9362 4 3.03218 0.7679 2 0.9123 0.9038 1 2.2959 0.3818 1 10.56188 0.8244 8 940 907 488 586 940 62.34043 0.9815 1 59 297 19.865320 0.9833 1 100 330 30.303030 0.79220 1 58 158 36.708861 0.34970 0 158 488 32.37705 0.60200 0 199 795 25.03145 0.8930 1 118 940 12.553192 0.7770 1 246 26.170213 0.90530 1 118 12.553192 0.08233 0 383 822.5089 46.56484 0.9984 1 30 197.8892 15.16000 0.5363 0 0 889 0.0000000 0.09479 0 898 940.3866 95.49264 0.9489 1 907 0 0.0000000 0.1079 0 2 0.2205072 0.4456 0 2 488 0.4098361 0.23670 0 146 487.6463 29.939736 0.9404 1 0 940 0.0000000 0.1831 0 4.23680 0.9379 4 2.61712 0.5593 2 0.9489 0.9409 1 1.91370 0.2196 1 9.71652 0.7468 8 0 0 0 0 0 Yes Census Tract 5, Calhoun County, Alabama 11742 38800 13571 38800 13620.72 45008 -49.72 -0.0036503 -6208 -0.1379310 NA NA Calhoun County, Alabama Anniston-Oxford, AL MSA C1150
01015000600 01015 000600 AL Alabama Calhoun County 3 South Region 6 East South Central Division 2571 992 796 1394 2133 65.35396 0.9789 1 263 905 29.060773 0.98990 1 121 306 39.54248 0.75940 1 209 490 42.65306 0.44810 0 330 796 41.45729 0.68030 0 641 1556 41.19537 0.9554 1 416 1760 23.636364 0.8383 1 220 8.556982 0.24910 0 584 22.714897 0.41610 0 539 1353 39.83740 0.9955 1 243 466 52.14592 0.9783 1 30 2366 1.2679628 0.48990 0 1944 2571 75.61260 0.8440 1 992 164 16.5322581 0.7673 1 8 0.8064516 0.5110 0 46 796 5.7788945 0.8329 1 184 796 23.115578 0.9049 1 614 2571 23.88176 0.9734 1 4.44280 0.9548 4 3.12890 0.8088 2 0.8440 0.8362 1 3.9895 0.9792 4 12.40520 0.9696 11 1950 964 719 837 1621 51.63479 0.9467 1 157 652 24.079755 0.9922 1 22 364 6.043956 0.01547 0 129 355 36.338028 0.34200 0 151 719 21.00139 0.23030 0 363 1387 26.17159 0.9048 1 351 1613 21.760694 0.9435 1 249 12.769231 0.32090 0 356 18.256410 0.27140 0 332 1259.7041 26.35540 0.9135 1 136 435.6156 31.22018 0.8775 1 0 1891 0.0000000 0.09479 0 1463 1949.9821 75.02633 0.8219 1 964 14 1.4522822 0.3459 0 8 0.8298755 0.5269 0 19 719 2.6425591 0.61120 0 197 719.0542 27.397100 0.9316 1 329 1950 16.8717949 0.9655 1 4.01750 0.9001 4 2.47809 0.4764 2 0.8219 0.8149 1 3.38110 0.8712 2 10.69859 0.8583 9 0 0 0 0 0 Yes Census Tract 6, Calhoun County, Alabama 10958 48000 14036 43300 12711.28 55680 1324.72 0.1042161 -12380 -0.2223420 NA NA Calhoun County, Alabama Anniston-Oxford, AL MSA C1150
01015002101 01015 002101 AL Alabama Calhoun County 3 South Region 6 East South Central Division 3872 1454 1207 1729 2356 73.38710 0.9916 1 489 2020 24.207921 0.97860 1 20 168 11.90476 0.02541 0 718 1039 69.10491 0.93320 1 738 1207 61.14333 0.96900 1 113 725 15.58621 0.6035 0 664 3943 16.839970 0.6495 0 167 4.313016 0.05978 0 238 6.146694 0.02255 0 264 2359 11.19118 0.3027 0 94 263 35.74144 0.9050 1 46 3769 1.2204829 0.48250 0 1601 3872 41.34814 0.6572 0 1454 761 52.3383769 0.9504 1 65 4.4704264 0.6738 0 5 1207 0.4142502 0.2791 0 113 1207 9.362055 0.7004 0 1516 3872 39.15289 0.9860 1 4.19220 0.9133 3 1.77253 0.1304 1 0.6572 0.6511 0 3.5897 0.9337 2 10.21163 0.7885 6 3238 1459 1014 1082 1836 58.93246 0.9735 1 251 1403 17.890235 0.9767 1 31 155 20.000000 0.44920 0 515 859 59.953434 0.85540 1 546 1014 53.84615 0.95350 1 134 916 14.62882 0.7033 0 251 3238 7.751699 0.5588 0 167 5.157505 0.03597 0 169 5.219271 0.02111 0 323 1667.0000 19.37612 0.7205 0 94 277.0000 33.93502 0.9040 1 0 3164 0.0000000 0.09479 0 1045 3238.0000 32.27301 0.5125 0 1459 607 41.6038382 0.9185 1 65 4.4551062 0.6949 0 24 1014 2.3668639 0.57900 0 85 1014.0000 8.382643 0.6775 0 1402 3238 43.2983323 0.9876 1 4.16580 0.9263 3 1.77637 0.1225 1 0.5125 0.5082 0 3.85750 0.9661 2 10.31217 0.8160 6 0 0 0 0 0 Yes Census Tract 21.01, Calhoun County, Alabama 4968 92000 9312 153500 5762.88 106720 3549.12 0.6158587 46780 0.4383433 NA NA Calhoun County, Alabama Anniston-Oxford, AL MSA C1150
01015002300 01015 002300 AL Alabama Calhoun County 3 South Region 6 East South Central Division 3882 1861 1608 1366 3882 35.18805 0.7753 1 186 1539 12.085770 0.80740 1 284 1109 25.60866 0.35530 0 202 499 40.48096 0.39670 0 486 1608 30.22388 0.34700 0 727 2610 27.85441 0.8534 1 547 3706 14.759849 0.5669 0 716 18.444101 0.82530 1 904 23.286966 0.45720 0 719 2919 24.63172 0.8986 1 207 1191 17.38035 0.5923 0 0 3720 0.0000000 0.09298 0 490 3882 12.62236 0.3118 0 1861 38 2.0419130 0.4070 0 199 10.6931757 0.7836 1 52 1608 3.2338308 0.6986 0 166 1608 10.323383 0.7304 0 0 3882 0.00000 0.3640 0 3.35000 0.7384 3 2.86638 0.6919 2 0.3118 0.3089 0 2.9836 0.7289 1 9.51178 0.7100 6 3265 1774 1329 1103 3265 33.78254 0.7880 1 122 1422 8.579465 0.8131 1 101 844 11.966825 0.10960 0 126 485 25.979381 0.15930 0 227 1329 17.08051 0.11070 0 267 2122 12.58247 0.6388 0 328 3265 10.045942 0.6808 0 440 13.476263 0.36070 0 843 25.819296 0.74470 0 530 2422.0000 21.88274 0.8097 1 254 861.0000 29.50058 0.8574 1 0 3026 0.0000000 0.09479 0 811 3265.0000 24.83920 0.4221 0 1774 7 0.3945885 0.2444 0 338 19.0529876 0.8924 1 19 1329 1.4296464 0.44520 0 120 1329.0000 9.029345 0.7016 0 0 3265 0.0000000 0.1831 0 3.03140 0.6608 2 2.86729 0.7016 2 0.4221 0.4185 0 2.46670 0.4669 1 8.78749 0.6230 5 0 0 0 0 0 Yes Census Tract 23, Calhoun County, Alabama 15086 77500 21540 78500 17499.76 89900 4040.24 0.2308740 -11400 -0.1268076 120.54 131.82 Calhoun County, Alabama Anniston-Oxford, AL MSA C1150
01023956700 01023 956700 AL Alabama Choctaw County 3 South Region 6 East South Central Division 3011 1772 1179 1715 3011 56.95782 0.9531 1 266 890 29.887640 0.99100 1 267 1035 25.79710 0.36240 0 79 144 54.86111 0.73440 0 346 1179 29.34690 0.31850 0 738 2053 35.94739 0.9287 1 543 2904 18.698347 0.7133 0 569 18.897376 0.84040 1 648 21.521089 0.33840 0 813 2273 35.76771 0.9901 1 252 771 32.68482 0.8778 1 0 2880 0.0000000 0.09298 0 2455 3011 81.53437 0.8712 1 1772 38 2.1444695 0.4136 0 485 27.3702032 0.9349 1 72 1179 6.1068702 0.8435 1 109 1179 9.245123 0.6964 0 0 3011 0.00000 0.3640 0 3.90460 0.8597 3 3.13968 0.8131 3 0.8712 0.8631 1 3.2524 0.8387 2 11.16788 0.8840 9 3335 1912 1362 1135 3313 34.25898 0.7948 1 188 1147 16.390584 0.9686 1 212 1058 20.037807 0.45090 0 27 304 8.881579 0.02679 0 239 1362 17.54772 0.12350 0 466 2537 18.36815 0.7948 1 495 3335 14.842579 0.8413 1 791 23.718141 0.85250 1 613 18.380810 0.27840 0 884 2714.0000 32.57185 0.9752 1 230 918.0000 25.05447 0.7925 1 25 3103 0.8056719 0.41920 0 2637 3335.0000 79.07046 0.8436 1 1912 0 0.0000000 0.1079 0 758 39.6443515 0.9799 1 16 1362 1.1747430 0.40060 0 75 1362.0000 5.506608 0.5316 0 8 3335 0.2398801 0.4965 0 3.52300 0.7901 4 3.31780 0.8870 3 0.8436 0.8365 1 2.51650 0.4924 1 10.20090 0.8033 9 0 0 0 0 0 Yes Census Tract 9567, Choctaw County, Alabama 12737 60900 16852 63400 14774.92 70644 2077.08 0.1405815 -7244 -0.1025423 NA NA NA NA NA
01023957000 01023 957000 AL Alabama Choctaw County 3 South Region 6 East South Central Division 2567 1187 916 767 2567 29.87924 0.6933 0 145 1060 13.679245 0.86050 1 101 719 14.04729 0.04540 0 43 197 21.82741 0.09791 0 144 916 15.72052 0.02333 0 355 1704 20.83333 0.7366 0 289 2296 12.587108 0.4736 0 324 12.621737 0.51120 0 688 26.801714 0.68810 0 572 1746 32.76060 0.9809 1 121 636 19.02516 0.6414 0 5 2283 0.2190101 0.22520 0 1314 2567 51.18816 0.7225 0 1187 0 0.0000000 0.1224 0 335 28.2224094 0.9394 1 13 916 1.4192140 0.4834 0 70 916 7.641921 0.6353 0 0 2567 0.00000 0.3640 0 2.78733 0.5903 1 3.04680 0.7745 1 0.7225 0.7158 0 2.5445 0.5114 1 9.10113 0.6601 3 2077 1158 866 759 2072 36.63127 0.8256 1 61 780 7.820513 0.7726 1 106 735 14.421769 0.19760 0 11 131 8.396947 0.02525 0 117 866 13.51039 0.04053 0 351 1464 23.97541 0.8815 1 205 2077 9.870005 0.6729 0 402 19.354839 0.68820 0 496 23.880597 0.63430 0 466 1576.0000 29.56853 0.9544 1 154 612.0000 25.16340 0.7942 1 0 2002 0.0000000 0.09479 0 1018 2077.0000 49.01300 0.6638 0 1158 0 0.0000000 0.1079 0 439 37.9101900 0.9766 1 0 866 0.0000000 0.09796 0 42 866.0000 4.849884 0.4884 0 5 2077 0.2407318 0.4971 0 3.19313 0.7061 3 3.16589 0.8369 2 0.6638 0.6582 0 2.16796 0.3247 1 9.19078 0.6792 6 0 0 0 0 0 Yes Census Tract 9570, Choctaw County, Alabama 16224 51600 21740 74000 18819.84 59856 2920.16 0.1551639 14144 0.2363005 NA NA NA NA NA
01031010500 01031 010500 AL Alabama Coffee County 3 South Region 6 East South Central Division 4529 1950 1664 1649 4022 40.99950 0.8432 1 114 1424 8.005618 0.56260 0 309 1057 29.23368 0.48130 0 251 607 41.35091 0.41690 0 560 1664 33.65385 0.45740 0 1269 3370 37.65579 0.9387 1 516 4279 12.058892 0.4492 0 832 18.370501 0.82310 1 894 19.739457 0.23950 0 1023 3404 30.05288 0.9666 1 303 1112 27.24820 0.8108 1 43 4270 1.0070258 0.44510 0 1761 4529 38.88276 0.6383 0 1950 6 0.3076923 0.2576 0 276 14.1538462 0.8279 1 8 1664 0.4807692 0.2925 0 125 1664 7.512019 0.6289 0 507 4529 11.19452 0.9441 1 3.25110 0.7138 2 3.28510 0.8639 3 0.6383 0.6324 0 2.9510 0.7136 2 10.12550 0.7794 7 4815 2118 1731 1329 4470 29.73154 0.7256 0 147 1903 7.724645 0.7670 1 209 1256 16.640127 0.29310 0 208 475 43.789474 0.51620 0 417 1731 24.09012 0.33700 0 953 3728 25.56330 0.8985 1 668 4485 14.894091 0.8425 1 1053 21.869159 0.79500 1 766 15.908619 0.16760 0 1010 3719.0000 27.15784 0.9262 1 243 1133.0000 21.44748 0.7184 0 1 4577 0.0218484 0.19150 0 1643 4815.0000 34.12253 0.5321 0 2118 0 0.0000000 0.1079 0 475 22.4268178 0.9157 1 37 1731 2.1374928 0.55080 0 144 1731.0000 8.318891 0.6750 0 330 4815 6.8535826 0.9282 1 3.57060 0.8018 3 2.79870 0.6649 2 0.5321 0.5276 0 3.17760 0.7990 2 10.07900 0.7892 7 0 0 0 0 0 Yes Census Tract 105, Coffee County, Alabama 14641 88000 21367 78100 16983.56 102080 4383.44 0.2580990 -23980 -0.2349138 128.88 137.26 Coffee County, Alabama Dothan-Enterprise-Ozark, AL CSA CS222

Log NMTC and LIHTC Variables

svi_national_nmtc_df$Median_Income_10adj_log <- log(svi_national_nmtc_df$Median_Income_10adj)
svi_national_nmtc_df$Median_Income_19_log <- log(svi_national_nmtc_df$Median_Income_19)

svi_national_nmtc_df$Median_Home_Value_10adj_log = log(svi_national_nmtc_df$Median_Home_Value_10adj)
svi_national_nmtc_df$Median_Home_Value_19_log = log(svi_national_nmtc_df$Median_Home_Value_19)

svi_national_nmtc_df$housing_price_index10_log = log(svi_national_nmtc_df$housing_price_index10)
svi_national_nmtc_df$housing_price_index20_log = log(svi_national_nmtc_df$housing_price_index20)

svi_divisional_nmtc_df$Median_Income_10adj_log <- log(svi_divisional_nmtc_df$Median_Income_10adj)
svi_divisional_nmtc_df$Median_Income_19_log <- log(svi_divisional_nmtc_df$Median_Income_19)

svi_divisional_nmtc_df$Median_Home_Value_10adj_log = log(svi_divisional_nmtc_df$Median_Home_Value_10adj)
svi_divisional_nmtc_df$Median_Home_Value_19_log = log(svi_divisional_nmtc_df$Median_Home_Value_19)

svi_divisional_nmtc_df$housing_price_index10_log = log(svi_divisional_nmtc_df$housing_price_index10)
svi_divisional_nmtc_df$housing_price_index20_log = log(svi_divisional_nmtc_df$housing_price_index20)

svi_national_lihtc_df$Median_Income_10adj_log <- log(svi_national_lihtc_df$Median_Income_10adj)
svi_national_lihtc_df$Median_Income_19_log <- log(svi_national_lihtc_df$Median_Income_19)

svi_national_lihtc_df$Median_Home_Value_10adj_log = log(svi_national_lihtc_df$Median_Home_Value_10adj)
svi_national_lihtc_df$Median_Home_Value_19_log = log(svi_national_lihtc_df$Median_Home_Value_19)

svi_national_lihtc_df$housing_price_index10_log = log(svi_national_lihtc_df$housing_price_index10)
svi_national_lihtc_df$housing_price_index20_log = log(svi_national_lihtc_df$housing_price_index20)

svi_divisional_lihtc_df$Median_Income_10adj_log <- log(svi_divisional_lihtc_df$Median_Income_10adj)
svi_divisional_lihtc_df$Median_Income_19_log <- log(svi_divisional_lihtc_df$Median_Income_19)

svi_divisional_lihtc_df$Median_Home_Value_10adj_log = log(svi_divisional_lihtc_df$Median_Home_Value_10adj)
svi_divisional_lihtc_df$Median_Home_Value_19_log = log(svi_divisional_lihtc_df$Median_Home_Value_19)

svi_divisional_lihtc_df$housing_price_index10_log = log(svi_divisional_lihtc_df$housing_price_index10)
svi_divisional_lihtc_df$housing_price_index20_log = log(svi_divisional_lihtc_df$housing_price_index20)

NMTC Variable Distribution: Mountain Division

SVI Theme 1: Socioeconomic Status

hist(svi_divisional_nmtc_df$F_THEME1_10)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME1_10)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME1_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME1_10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME1_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME1_10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME1_10))))
## [1] "Absolute Skewness: 0.0249901327808313"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME1_10))))
## [1] "Absolute Excess Kurtosis: 1.12999825726"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME1_10) < mean(svi_divisional_nmtc_df$F_THEME1_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2020

hist(svi_divisional_nmtc_df$F_THEME1_20)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME1_20)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME1_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME1_20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME1_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME1_20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME1_20))))
## [1] "Absolute Skewness: 0.0216149135904862"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME1_20))))
## [1] "Absolute Excess Kurtosis: 1.03461573701947"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME1_20) < mean(svi_divisional_nmtc_df$F_THEME1_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

SVI THeme 2: Household Characteristics

2010

hist(svi_divisional_nmtc_df$F_THEME2_10)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME2_10)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME2_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME2_10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME2_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME2_10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME2_10))))
## [1] "Absolute Skewness: 0.00100423682188465"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME2_10))))
## [1] "Absolute Excess Kurtosis: 0.651194090353722"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME2_10) < mean(svi_divisional_nmtc_df$F_THEME2_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2020

hist(svi_divisional_nmtc_df$F_THEME2_20)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME2_20)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME2_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME2_20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME2_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME2_20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME2_20))))
## [1] "Absolute Skewness: 0.0215143038130277"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME2_20))))
## [1] "Absolute Excess Kurtosis: 0.701346689837882"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME2_20) < mean(svi_divisional_nmtc_df$F_THEME2_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

SVI Theme 3: Racial & Ethnic Minority Status

2010

hist(svi_divisional_nmtc_df$F_THEME3_10)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME3_10)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME3_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME3_10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME1_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME3_10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME3_10))))
## [1] "Absolute Skewness: 0.0309634623783316"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME3_10))))
## [1] "Absolute Excess Kurtosis: 1.99904126399755"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME3_10) < mean(svi_divisional_nmtc_df$F_THEME3_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"
table(svi_divisional_nmtc_df$F_THEME3_10)
## 
##   0   1 
## 984 954

2020

hist(svi_divisional_nmtc_df$F_THEME3_20)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME3_20)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME3_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME3_20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME3_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME3_20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME3_20))))
## [1] "Absolute Skewness: 0.0805606315825733"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME3_20))))
## [1] "Absolute Excess Kurtosis: 1.99350998463902"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME3_20) < mean(svi_divisional_nmtc_df$F_THEME3_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"
table(svi_divisional_nmtc_df$F_THEME3_20)
## 
##    0    1 
## 1008  930

SVI Theme 4: Housing Type & Transportation

2010

hist(svi_divisional_nmtc_df$F_THEME4_10)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME4_10)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME4_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME4_10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME4_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME4_10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME4_10))))
## [1] "Absolute Skewness: 0.0628118773173425"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME4_10))))
## [1] "Absolute Excess Kurtosis: 0.578223205433793"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME4_10) < mean(svi_divisional_nmtc_df$F_THEME4_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2020

hist(svi_divisional_nmtc_df$F_THEME4_20)

plotNormalHistogram(svi_divisional_nmtc_df$F_THEME4_20)

ggdensity(svi_divisional_nmtc_df, x = "F_THEME4_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_THEME4_20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_THEME4_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_THEME4_20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_THEME4_20))))
## [1] "Absolute Skewness: 0.0605950491829714"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_THEME4_20))))
## [1] "Absolute Excess Kurtosis: 0.658673836104457"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_THEME4_20) < mean(svi_divisional_nmtc_df$F_THEME4_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

SVI OVerall

2010

hist(svi_divisional_nmtc_df$F_TOTAL_10)

plotNormalHistogram(svi_divisional_nmtc_df$F_TOTAL_10)

ggdensity(svi_divisional_nmtc_df, x = "F_TOTAL_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_TOTAL_10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_TOTAL_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_TOTAL_10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_TOTAL_10))))
## [1] "Absolute Skewness: 0.0311483749255969"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_TOTAL_10))))
## [1] "Absolute Excess Kurtosis: 1.04070433814952"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_TOTAL_10) < mean(svi_divisional_nmtc_df$F_TOTAL_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2020

hist(svi_divisional_nmtc_df$F_TOTAL_20)

plotNormalHistogram(svi_divisional_nmtc_df$F_TOTAL_20)

ggdensity(svi_divisional_nmtc_df, x = "F_TOTAL_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$F_TOTAL_20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$F_TOTAL_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$F_TOTAL_20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$F_TOTAL_20))))
## [1] "Absolute Skewness: 0.0629866656842071"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$F_TOTAL_20))))
## [1] "Absolute Excess Kurtosis: 0.878387884554749"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$F_TOTAL_20) < mean(svi_divisional_nmtc_df$F_TOTAL_20)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Median Income

2010

options(scipen = 999)
hist(svi_divisional_nmtc_df$Median_Income_10adj)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Income_10adj)

ggdensity(svi_divisional_nmtc_df, x = "Median_Income_10adj", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$Median_Income_10adj, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Income_10adj, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Income_10adj)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Income_10adj, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.139463092047866"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Income_10adj, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.652592362975045"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Income_10adj, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Income_10adj, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2010 Log

hist(svi_divisional_nmtc_df$Median_Income_10adj_log)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Income_10adj_log)

ggdensity(svi_divisional_nmtc_df, x = "Median_Income_10adj_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$Median_Income_10adj_log, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Income_10adj_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Income_10adj_log)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Income_10adj_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.42384668286417"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Income_10adj_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 5.55272518758958"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Income_10adj_log, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Income_10adj_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2019

options(scipen = 999)
hist(svi_divisional_nmtc_df$Median_Income_19)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Income_19)

ggdensity(svi_divisional_nmtc_df, x = "Median_Income_19", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_nmtc_df$Median_Income_19, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Income_19, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Income_19)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Income_19, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.124785761521551"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Income_19, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.973068468347"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Income_19, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Income_19, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Median Home Value

2010

hist(svi_divisional_nmtc_df$Median_Home_Value_10adj)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Home_Value_10adj)

ggdensity(svi_divisional_nmtc_df, x = "Median_Home_Value_10adj", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$Median_Home_Value_10adj, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Home_Value_10adj, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Home_Value_10adj)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Home_Value_10adj, na.rm = TRUE))))
## [1] "Absolute Skewness: 2.29498564482883"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Home_Value_10adj, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 16.4426441276446"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Home_Value_10adj, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Home_Value_10adj, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2010 Log

hist(svi_divisional_nmtc_df$Median_Home_Value_10adj_log)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Home_Value_10adj_log)

ggdensity(svi_divisional_nmtc_df, x = "Median_Home_Value_10adj_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 30 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Home_Value_10adj_log)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.17511485647903"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 3.40043121085217"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Home_Value_10adj_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2019

hist(svi_divisional_nmtc_df$Median_Home_Value_19)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Home_Value_19)

ggdensity(svi_divisional_nmtc_df, x = "Median_Home_Value_19", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$Median_Home_Value_19, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Home_Value_19, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Home_Value_19)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Home_Value_19, na.rm = TRUE))))
## [1] "Absolute Skewness: 2.60320862844623"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Home_Value_19, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 18.5791808764468"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Home_Value_19, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Home_Value_19, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2019 Log

hist(svi_divisional_nmtc_df$Median_Home_Value_19_log)

plotNormalHistogram(svi_divisional_nmtc_df$Median_Home_Value_19_log)

ggdensity(svi_divisional_nmtc_df, x = "Median_Home_Value_19_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$Median_Home_Value_19_log, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$Median_Home_Value_19_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$Median_Home_Value_19_log)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$Median_Home_Value_19_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.862701684939476"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$Median_Home_Value_19_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 2.58686759381741"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$Median_Home_Value_19_log, na.rm = TRUE) < mean(svi_divisional_nmtc_df$Median_Home_Value_19_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Housing Price Index

*2010

hist(svi_divisional_nmtc_df$housing_price_index10)

plotNormalHistogram(svi_divisional_nmtc_df$housing_price_index10)

ggdensity(svi_divisional_nmtc_df, x = "housing_price_index10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 857 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 857 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$housing_price_index10, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$housing_price_index10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$housing_price_index10)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.13858022197036"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.13858022197036"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 2.36895681000769"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$housing_price_index10, na.rm = TRUE) < mean(svi_divisional_nmtc_df$housing_price_index10, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2010 Log

hist(svi_divisional_nmtc_df$housing_price_index10_log)

plotNormalHistogram(svi_divisional_nmtc_df$housing_price_index10_log)

ggdensity(svi_divisional_nmtc_df, x = "housing_price_index10_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 857 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 857 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$housing_price_index10_log, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$housing_price_index10_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$housing_price_index10_log)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$housing_price_index10_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.0435644823884166"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$housing_price_index10_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.0142493700199102"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$housing_price_index10_log, na.rm = TRUE) < mean(svi_divisional_nmtc_df$housing_price_index10_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020

hist(svi_divisional_nmtc_df$housing_price_index20)

plotNormalHistogram(svi_divisional_nmtc_df$housing_price_index20)

ggdensity(svi_divisional_nmtc_df, x = "housing_price_index20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 722 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 722 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$housing_price_index20, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$housing_price_index20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$housing_price_index20)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$housing_price_index20, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.33009328321753"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$housing_price_index20, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 3.08862562179683"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$housing_price_index20, na.rm = TRUE) < mean(svi_divisional_nmtc_df$housing_price_index20, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020 Log

hist(svi_divisional_nmtc_df$housing_price_index20_log)

plotNormalHistogram(svi_divisional_nmtc_df$housing_price_index20_log)

ggdensity(svi_divisional_nmtc_df, x = "housing_price_index20_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 722 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 722 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_nmtc_df$housing_price_index20_log, pch = 1, frame = FALSE)
qqline(svi_divisional_nmtc_df$housing_price_index20_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_nmtc_df$housing_price_index20_log)))
## [1] "Length: 1938"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_nmtc_df$housing_price_index20_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.0722867843904414"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_nmtc_df$housing_price_index20_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.0755339224588782"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_nmtc_df$housing_price_index20_log, na.rm = TRUE) < mean(svi_divisional_nmtc_df$housing_price_index20_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

LIHTC Variable Distribution: Mountain Division

SVI Theme 1: Socioeconomic Status

hist(svi_divisional_lihtc_df$F_THEME1_10)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME1_10)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME1_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME1_10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME1_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME1_10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME1_10))))
## [1] "Absolute Skewness: 0.698742055986755"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME1_10))))
## [1] "Absolute Excess Kurtosis: 0.299137459182033"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME1_10) < mean(svi_divisional_lihtc_df$F_THEME1_10)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020

hist(svi_divisional_lihtc_df$F_THEME1_20)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME1_20)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME1_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME1_20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME1_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME1_20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME1_20))))
## [1] "Absolute Skewness: 0.591300029458139"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME1_20))))
## [1] "Absolute Excess Kurtosis: 0.587568327007457"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME1_20) < mean(svi_divisional_lihtc_df$F_THEME1_20)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

SVI THeme 2: Household Characteristics

2010

hist(svi_divisional_lihtc_df$F_THEME2_10)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME2_10)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME2_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME2_10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME2_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME2_10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME2_10))))
## [1] "Absolute Skewness: 0.282745286649261"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME2_10))))
## [1] "Absolute Excess Kurtosis: 0.916403920458291"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME2_10) < mean(svi_divisional_lihtc_df$F_THEME2_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2020

hist(svi_divisional_lihtc_df$F_THEME2_20)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME2_20)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME2_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME2_20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME2_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME2_20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME2_20))))
## [1] "Absolute Skewness: 0.323066588839606"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME2_20))))
## [1] "Absolute Excess Kurtosis: 0.949799629112459"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME2_20) < mean(svi_divisional_lihtc_df$F_THEME2_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

SVI Theme 3: Racial & Ethnic Minority Status

2010

hist(svi_divisional_lihtc_df$F_THEME3_10)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME3_10)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME3_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME3_10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME1_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME3_10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME3_10))))
## [1] "Absolute Skewness: 0.812149380607502"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME3_10))))
## [1] "Absolute Excess Kurtosis: 1.34041338357885"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME3_10) < mean(svi_divisional_lihtc_df$F_THEME3_10)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"
table(svi_divisional_lihtc_df$F_THEME3_10)
## 
##   0   1 
##  63 139

2020

hist(svi_divisional_lihtc_df$F_THEME3_20)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME3_20)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME3_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME3_20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME3_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME3_20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME3_20))))
## [1] "Absolute Skewness: 0.812149380607502"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME3_20))))
## [1] "Absolute Excess Kurtosis: 1.34041338357885"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME3_20) < mean(svi_divisional_lihtc_df$F_THEME3_20)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"
table(svi_divisional_lihtc_df$F_THEME3_20)
## 
##   0   1 
##  63 139

SVI Theme 4: Housing Type & Transportation

2010

hist(svi_divisional_lihtc_df$F_THEME4_10)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME4_10)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME4_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME4_10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME4_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME4_10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME4_10))))
## [1] "Absolute Skewness: 0.314984102241371"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME4_10))))
## [1] "Absolute Excess Kurtosis: 0.215212929257489"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME4_10) < mean(svi_divisional_lihtc_df$F_THEME4_10)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020

hist(svi_divisional_lihtc_df$F_THEME4_20)

plotNormalHistogram(svi_divisional_lihtc_df$F_THEME4_20)

ggdensity(svi_divisional_lihtc_df, x = "F_THEME4_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_THEME4_20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_THEME4_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_THEME4_20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_THEME4_20))))
## [1] "Absolute Skewness: 0.192855546387606"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_THEME4_20))))
## [1] "Absolute Excess Kurtosis: 0.327891361511281"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_THEME4_20) < mean(svi_divisional_lihtc_df$F_THEME4_20)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

SVI Overall

2010

hist(svi_divisional_lihtc_df$F_TOTAL_10)

plotNormalHistogram(svi_divisional_lihtc_df$F_TOTAL_10)

ggdensity(svi_divisional_lihtc_df, x = "F_TOTAL_10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_TOTAL_10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_TOTAL_10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_TOTAL_10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_TOTAL_10))))
## [1] "Absolute Skewness: 0.525398616902496"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_TOTAL_10))))
## [1] "Absolute Excess Kurtosis: 0.467799904292678"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_TOTAL_10) < mean(svi_divisional_lihtc_df$F_TOTAL_10)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020

hist(svi_divisional_lihtc_df$F_TOTAL_20)

plotNormalHistogram(svi_divisional_lihtc_df$F_TOTAL_20)

ggdensity(svi_divisional_lihtc_df, x = "F_TOTAL_20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$F_TOTAL_20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$F_TOTAL_20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$F_TOTAL_20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$F_TOTAL_20))))
## [1] "Absolute Skewness: 0.430674795613127"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$F_TOTAL_20))))
## [1] "Absolute Excess Kurtosis: 0.280822546693426"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$F_TOTAL_20) < mean(svi_divisional_lihtc_df$F_TOTAL_20)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Median Income

2010

options(scipen = 999)
hist(svi_divisional_lihtc_df$Median_Income_10adj)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Income_10adj)

ggdensity(svi_divisional_lihtc_df, x = "Median_Income_10adj", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$Median_Income_10adj, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Income_10adj, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Income_10adj)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Income_10adj, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.00188005391528848"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Income_10adj, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.991594713590978"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Income_10adj, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Income_10adj, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2010 Log

hist(svi_divisional_lihtc_df$Median_Income_10adj_log)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Income_10adj_log)

ggdensity(svi_divisional_lihtc_df, x = "Median_Income_10adj_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$Median_Income_10adj_log, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Income_10adj_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Income_10adj_log)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Income_10adj_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.54255461177144"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Income_10adj_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 2.73888280996131"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Income_10adj_log, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Income_10adj_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2019

options(scipen = 999)
hist(svi_divisional_lihtc_df$Median_Income_19)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Income_19)

ggdensity(svi_divisional_lihtc_df, x = "Median_Income_19", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")

qqnorm(svi_divisional_lihtc_df$Median_Income_19, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Income_19, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Income_19)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Income_19, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.738967218711519"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Income_19, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 3.96758907065596"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Income_19, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Income_19, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Median Home Value

2010

hist(svi_divisional_lihtc_df$Median_Home_Value_10adj)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Home_Value_10adj)

ggdensity(svi_divisional_lihtc_df, x = "Median_Home_Value_10adj", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$Median_Home_Value_10adj, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Home_Value_10adj, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Home_Value_10adj)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Home_Value_10adj, na.rm = TRUE))))
## [1] "Absolute Skewness: 2.9478613739634"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Home_Value_10adj, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 13.6859110055077"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Home_Value_10adj, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Home_Value_10adj, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2010 Log

hist(svi_divisional_lihtc_df$Median_Home_Value_10adj_log)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Home_Value_10adj_log)

ggdensity(svi_divisional_lihtc_df, x = "Median_Home_Value_10adj_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Home_Value_10adj_log)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.399708664679192"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 1.67692481223881"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Home_Value_10adj_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2019

hist(svi_divisional_lihtc_df$Median_Home_Value_19)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Home_Value_19)

ggdensity(svi_divisional_lihtc_df, x = "Median_Home_Value_19", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$Median_Home_Value_19, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Home_Value_19, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Home_Value_19)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Home_Value_19, na.rm = TRUE))))
## [1] "Absolute Skewness: 3.19090980091081"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Home_Value_19, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 17.6985347895785"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Home_Value_19, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Home_Value_19, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: FALSE"

2019 Log

hist(svi_divisional_lihtc_df$Median_Home_Value_19_log)

plotNormalHistogram(svi_divisional_lihtc_df$Median_Home_Value_19_log)

ggdensity(svi_divisional_lihtc_df, x = "Median_Home_Value_19_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$Median_Home_Value_19_log, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$Median_Home_Value_19_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$Median_Home_Value_19_log)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$Median_Home_Value_19_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.0237161694371718"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$Median_Home_Value_19_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.202114576132213"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$Median_Home_Value_19_log, na.rm = TRUE) < mean(svi_divisional_lihtc_df$Median_Home_Value_19_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Housing Price Index

*2010

hist(svi_divisional_lihtc_df$housing_price_index10)

plotNormalHistogram(svi_divisional_lihtc_df$housing_price_index10)

ggdensity(svi_divisional_lihtc_df, x = "housing_price_index10", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 136 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 136 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$housing_price_index10, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$housing_price_index10, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$housing_price_index10)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.45735350547226"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.45735350547226"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$housing_price_index10, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 2.4013271457039"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$housing_price_index10, na.rm = TRUE) < mean(svi_divisional_lihtc_df$housing_price_index10, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2010 Log

hist(svi_divisional_lihtc_df$housing_price_index10_log)

plotNormalHistogram(svi_divisional_lihtc_df$housing_price_index10_log)

ggdensity(svi_divisional_lihtc_df, x = "housing_price_index10_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 136 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 136 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$housing_price_index10_log, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$housing_price_index10_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$housing_price_index10_log)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$housing_price_index10_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.323584982353486"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$housing_price_index10_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.235071725246949"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$housing_price_index10_log, na.rm = TRUE) < mean(svi_divisional_lihtc_df$housing_price_index10_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020

hist(svi_divisional_lihtc_df$housing_price_index20)

plotNormalHistogram(svi_divisional_lihtc_df$housing_price_index20)

ggdensity(svi_divisional_lihtc_df, x = "housing_price_index20", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 113 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 113 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$housing_price_index20, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$housing_price_index20, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$housing_price_index20)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$housing_price_index20, na.rm = TRUE))))
## [1] "Absolute Skewness: 1.48182201048028"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$housing_price_index20, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 2.32450252812465"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$housing_price_index20, na.rm = TRUE) < mean(svi_divisional_lihtc_df$housing_price_index20, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

2020 Log

hist(svi_divisional_lihtc_df$housing_price_index20_log)

plotNormalHistogram(svi_divisional_lihtc_df$housing_price_index20_log)

ggdensity(svi_divisional_lihtc_df, x = "housing_price_index20_log", fill = "lightgray") +
  stat_overlay_normal_density(color = "red", linetype = "dashed")
## Warning: Removed 113 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 113 rows containing non-finite outside the scale range
## (`stat_overlay_normal_density()`).

qqnorm(svi_divisional_lihtc_df$housing_price_index20_log, pch = 1, frame = FALSE)
qqline(svi_divisional_lihtc_df$housing_price_index20_log, col = "steelblue", lwd = 2)

# Statistics

print(paste0("Length: ", length(svi_divisional_lihtc_df$housing_price_index20_log)))
## [1] "Length: 202"
print(paste0("Absolute Skewness: ", abs(skewness(svi_divisional_lihtc_df$housing_price_index20_log, na.rm = TRUE))))
## [1] "Absolute Skewness: 0.25859338485172"
print(paste0("Absolute Excess Kurtosis: ", abs(3 - kurtosis(svi_divisional_lihtc_df$housing_price_index20_log, na.rm = TRUE))))
## [1] "Absolute Excess Kurtosis: 0.237165858523208"
print(paste0("Standard deviation is less than 1/2 mean: ", sd(svi_divisional_lihtc_df$housing_price_index20_log, na.rm = TRUE) < mean(svi_divisional_lihtc_df$housing_price_index20_log, na.rm = TRUE)/2))
## [1] "Standard deviation is less than 1/2 mean: TRUE"

Differences-in-Differences Models

NMTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
nmtc_did10_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(nmtc_did10_div_svi)
## [1] 1938
# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
nmtc_did20_div_svi <- svi_divisional_nmtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(nmtc_did20_div_svi)
## [1] 1938
nmtc_diff_in_diff_div_svi <- bind_rows(nmtc_did10_div_svi, nmtc_did20_div_svi)

nmtc_diff_in_diff_div_svi <- nmtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_svi)
## [1] 3876

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
nmtc_did10_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(nmtc_did10_div_inc)
## [1] 1938
# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
nmtc_did19_div_inc <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(nmtc_did19_div_inc)
## [1] 1938
nmtc_diff_in_diff_div_inc <- bind_rows(nmtc_did10_div_inc, nmtc_did19_div_inc)

nmtc_diff_in_diff_div_inc <- nmtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_inc)
## [1] 3876
nmtc_diff_in_diff_div_svi %>% head() %>% kbl() %>% kable_styling() %>% scroll_box(width = "100%")
GEOID_2010_trt cbsa SVI_FLAG_COUNT_SES SVI_FLAG_COUNT_HHCHAR SVI_FLAG_COUNT_REM SVI_FLAG_COUNT_HOUSETRANSPT SVI_FLAG_COUNT_OVERALL treat post year
04001942600 NA 2 4 1 3 10 0 0 2010
04001942700 NA 4 4 1 3 12 0 0 2010
04001944000 NA 3 1 1 3 8 0 0 2010
04001944100 NA 4 4 1 3 12 0 0 2010
04001944202 NA 4 3 1 4 12 0 0 2010
04001944300 NA 4 4 1 4 13 0 0 2010

Divisional Home Value

nmtc_did10_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(nmtc_did10_div_mhv)
## [1] 1882
nmtc_did19_div_mhv <- svi_divisional_nmtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "nmtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(nmtc_did19_div_mhv)
## [1] 1882
nmtc_diff_in_diff_div_mhv <- bind_rows(nmtc_did10_div_mhv, nmtc_did19_div_mhv)

nmtc_diff_in_diff_div_mhv <- nmtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_mhv)
## [1] 3764

Divisional House Price Index

nmtc_did10_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, nmtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(nmtc_did10_div_hpi)
## [1] 1080
nmtc_did20_div_hpi <- svi_divisional_nmtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, nmtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "nmtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(nmtc_did20_div_hpi)
## [1] 1080
nmtc_diff_in_diff_div_hpi <- bind_rows(nmtc_did10_div_hpi, nmtc_did20_div_hpi)

nmtc_diff_in_diff_div_hpi <- nmtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(nmtc_diff_in_diff_div_hpi)
## [1] 2160

NMTC Divisional Model

# SVI & Economic Models

m1_nmtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m2_nmtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m3_nmtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m4_nmtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi )

m5_nmtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_svi)

m6_nmtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_inc )

m7_nmtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_mhv )

m8_nmtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=nmtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_nmtc_div,
  "HHChar"  = m2_nmtc_div,
  "REM" = m3_nmtc_div,
  "HOUSETRANSPT" = m4_nmtc_div,
  "OVERALL" = m5_nmtc_div,
  "Median Income (USD, logged)" = m6_nmtc_div,
  "Median Home Value (USD, logged)" = m7_nmtc_div,
  "House Price Index (logged)" = m8_nmtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of NMTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))
Social Vulnerability Economic Outcomes
Differences-in-Differences Linear Regression Analysis of NMTC in Mountain Division
SES HHChar REM HOUSETRANSPT OVERALL Median Income (USD, logged) Median Home Value (USD, logged) House Price Index (logged)
* p < 0.05, ** p < 0.01, *** p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).
(Intercept) 2.22*** 2.27*** 0.44*** 1.74*** 6.67*** 9.87*** 11.43*** 4.93***
(0.36) (0.25) (0.10) (0.27) (0.73) (0.07) (0.12) (0.03)
treat 0.43** 0.10 0.05 0.48*** 1.06*** -0.09*** -0.05 -0.07
(0.14) (0.09) (0.04) (0.10) (0.28) (0.03) (0.04) (0.04)
post -0.07 -0.04 -0.01 0.03 -0.10 0.00 -0.05** 0.61***
(0.05) (0.04) (0.01) (0.04) (0.10) (0.01) (0.02) (0.01)
treat × post 0.02 0.00 -0.00 0.09 0.11 0.03 0.05 -0.00
(0.19) (0.13) (0.05) (0.14) (0.38) (0.04) (0.06) (0.06)
Num.Obs. 3436 3436 3436 3436 3436 3436 3324 1974
R2 0.185 0.187 0.342 0.113 0.235 0.171 0.257 0.610
R2 Adj. 0.167 0.169 0.327 0.093 0.218 0.153 0.240 0.596
RMSE 1.41 0.99 0.41 1.05 2.87 0.27 0.45 0.30

In looking at our social vulnerability index models, we see that there were no categories in the Mountain Division that experienced statistically significant changes in regards to those counties receiving NMTC funding. In particular, we are looking for statistically significant changes in the treat x post variable and none exist in the Mountain Division.

In evaluating the indicators of economic conditions, again we do not see any significantly significant changes in economic outcomes in counties receiving NMTC funding versus those that did not.

Visualize SES

status <- c("NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant", 
             "NMTC Non-Participant", 
             "NMTC Participant Counterfactual", 
             "NMTC Participant")
year <- c(2010, 
          2010, 
          2010, 
          2020, 
          2020, 
          2020)
outcome <- c(m1_nmtc_div$coefficients[1], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2],
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[3], 
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2] + m1_nmtc_div$coefficients[3],
           m1_nmtc_div$coefficients[1] + m1_nmtc_div$coefficients[2] + m1_nmtc_div$coefficients[3] + m1_nmtc_div$coefficients[length(m1_nmtc_div$coefficients)])

svidiv_viz_ses_nmtc <- data.frame(status, year, outcome)
svidiv_viz_ses_nmtc$outcome_label <- round(svidiv_viz_ses_nmtc$outcome, 2)
svidiv_viz_ses_nmtc
##                            status year  outcome outcome_label
## 1            NMTC Non-Participant 2010 2.221420          2.22
## 2 NMTC Participant Counterfactual 2010 2.654338          2.65
## 3                NMTC Participant 2010 2.654338          2.65
## 4            NMTC Non-Participant 2020 2.153580          2.15
## 5 NMTC Participant Counterfactual 2020 2.586499          2.59
## 6                NMTC Participant 2020 2.606719          2.61
slopegraph_plot(svidiv_viz_ses_nmtc, "NMTC Participant", "NMTC Non-Participant","Impact of NMTC Program on SVI SES Flag Count", paste0(census_division, " | 2010 - 2020"))

The slopegraph for SES SVI flags for the NMTC program indicates that in the Mountain Division our NMTC Participant Tracts did not experience a notable decrease in socioeconomic social vulnerability flags in 2020 from the expected count of 2.59 for the counterfactual to 2.61 for the actual outcome.

Because we do not have any statistically significants changes in our outcomes we not need to visualize the counterfactual and outcome graphs. This slopegraph for the SES flag was included for demonstration.

LIHTC Evaluation

Divisional SVI

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010
lihtc_did10_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_10, F_THEME2_10, F_THEME3_10, F_THEME4_10, F_TOTAL_10, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_10",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_10",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_10",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_10",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_10") 

nrow(lihtc_did10_div_svi)
## [1] 202
# Create 2020 df, create post variable and set to 1, create year variable and set to 2020
lihtc_did20_div_svi <- svi_divisional_lihtc_df %>% 
  select(GEOID_2010_trt, cbsa, F_THEME1_20, F_THEME2_20, F_THEME3_20, F_THEME4_20, F_TOTAL_20, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "SVI_FLAG_COUNT_SES" = "F_THEME1_20",
         "SVI_FLAG_COUNT_HHCHAR" = "F_THEME2_20",
         "SVI_FLAG_COUNT_REM" = "F_THEME3_20",
         "SVI_FLAG_COUNT_HOUSETRANSPT" = "F_THEME4_20",
         "SVI_FLAG_COUNT_OVERALL" = "F_TOTAL_20"
  )


nrow(lihtc_did20_div_svi)
## [1] 202
lihtc_diff_in_diff_div_svi <- bind_rows(lihtc_did10_div_svi, lihtc_did20_div_svi)

lihtc_diff_in_diff_div_svi <- lihtc_diff_in_diff_div_svi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_svi)
## [1] 404

Divisional Median Income

# Create 2010 df, create post variable and set to 0, create year variable and set to 2010, remove any tracts that don't have data for 2010 and 2019
lihtc_did10_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_10adj_log") 


nrow(lihtc_did10_div_inc)
## [1] 202
# Create 2019 df, create post variable and set to 1, create year variable and set to 2019, remove any tracts that don't have data for 2010 and 2019
lihtc_did19_div_inc <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Income_10adj_log)) %>% filter(!is.na(Median_Income_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Income_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_INCOME" = "Median_Income_19_log") 


nrow(lihtc_did19_div_inc)
## [1] 202
lihtc_diff_in_diff_div_inc <- bind_rows(lihtc_did10_div_inc, lihtc_did19_div_inc)

lihtc_diff_in_diff_div_inc <- lihtc_diff_in_diff_div_inc %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_inc)
## [1] 404

Divisional Median Home Value

lihtc_did10_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_10adj_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_10adj_log") 


nrow(lihtc_did10_div_mhv)
## [1] 187
lihtc_did19_div_mhv <- svi_divisional_lihtc_df %>% 
  filter(!is.na(Median_Home_Value_10adj_log)) %>% filter(!is.na(Median_Home_Value_19_log)) %>%
  select(GEOID_2010_trt, cbsa, Median_Home_Value_19_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2019) %>%
  rename("treat" = "lihtc_flag",
         "MEDIAN_HOME_VALUE" = "Median_Home_Value_19_log") 


nrow(lihtc_did19_div_mhv)
## [1] 187
lihtc_diff_in_diff_div_mhv <- bind_rows(lihtc_did10_div_mhv, lihtc_did19_div_mhv)

lihtc_diff_in_diff_div_mhv <- lihtc_diff_in_diff_div_mhv %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_mhv)
## [1] 374

Divisional House Price Index

lihtc_did10_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index10_log, lihtc_flag) %>% 
  mutate(post = 0,
         year = 2010) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index10_log") 


nrow(lihtc_did10_div_hpi)
## [1] 66
lihtc_did20_div_hpi <- svi_divisional_lihtc_df %>% 
  filter(!is.na(housing_price_index10_log)) %>% filter(!is.na(housing_price_index20_log)) %>%
  select(GEOID_2010_trt, cbsa, housing_price_index20_log, lihtc_flag) %>% 
  mutate(post = 1,
         year = 2020) %>%
  rename("treat" = "lihtc_flag",
         "HOUSE_PRICE_INDEX" = "housing_price_index20_log") 


nrow(lihtc_did20_div_hpi)
## [1] 66
lihtc_diff_in_diff_div_hpi <- bind_rows(lihtc_did10_div_hpi, lihtc_did20_div_hpi)

lihtc_diff_in_diff_div_hpi <- lihtc_diff_in_diff_div_hpi %>% arrange(post, treat, GEOID_2010_trt)

nrow(lihtc_diff_in_diff_div_hpi)
## [1] 132

LIHTC Divisional Model

# SVI & Economic Models

m1_lihtc_div <- lm( SVI_FLAG_COUNT_SES ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m2_lihtc_div <- lm( SVI_FLAG_COUNT_HHCHAR ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m3_lihtc_div <- lm( SVI_FLAG_COUNT_REM ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m4_lihtc_div <- lm( SVI_FLAG_COUNT_HOUSETRANSPT ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi )

m5_lihtc_div <- lm( SVI_FLAG_COUNT_OVERALL  ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_svi)

m6_lihtc_div <- lm( MEDIAN_INCOME ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_inc )

m7_lihtc_div <- lm( MEDIAN_HOME_VALUE ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_mhv )

m8_lihtc_div <- lm( HOUSE_PRICE_INDEX ~ treat + post + treat*post + cbsa, data=lihtc_diff_in_diff_div_hpi )

# Add all models to a list
models <- list(
  
  "SES" = m1_lihtc_div,
  "HHChar"  = m2_lihtc_div,
  "REM" = m3_lihtc_div,
  "HOUSETRANSPT" = m4_lihtc_div,
  "OVERALL" = m5_lihtc_div,
  "Median Income (USD, logged)" = m6_lihtc_div,
  "Median Home Value (USD, logged)" = m7_lihtc_div,
  "House Price Index (logged)" = m8_lihtc_div
)


# Display model results
modelsummary(models,  fmt = 2, stars = c('*' = .05, '**' = .01, '***' = .001), coef_omit = "cbsa", gof_omit = "IC|Log",
             notes = list('All models include metro-level fixed effects by core-based statistical area (cbsa).'),
             title = paste0("Differences-in-Differences Linear Regression Analysis of LIHTC in ", census_division)) %>%
  group_tt(j = list("Social Vulnerability" = 2:6, "Economic Outcomes" = 7:9))
Social Vulnerability Economic Outcomes
Differences-in-Differences Linear Regression Analysis of LIHTC in Mountain Division
SES HHChar REM HOUSETRANSPT OVERALL Median Income (USD, logged) Median Home Value (USD, logged) House Price Index (logged)
* p < 0.05, ** p < 0.01, *** p < 0.001
All models include metro-level fixed effects by core-based statistical area (cbsa).
(Intercept) 3.44*** 1.87*** 0.84*** 1.93*** 8.09*** 9.77*** 11.96*** 4.84***
(0.19) (0.18) (0.06) (0.17) (0.38) (0.07) (0.08) (0.09)
treat -0.05 0.10 -0.05 -0.06 -0.05 0.11 0.07 0.20
(0.23) (0.22) (0.07) (0.21) (0.48) (0.09) (0.11) (0.16)
post -0.12 -0.07 0.01 0.02 -0.16 0.02 -0.07 0.62***
(0.11) (0.11) (0.03) (0.10) (0.23) (0.04) (0.05) (0.06)
treat × post -0.26 -0.06 -0.01 0.19 -0.13 0.04 0.04 0.03
(0.31) (0.30) (0.10) (0.28) (0.64) (0.12) (0.15) (0.20)
Num.Obs. 372 372 372 372 372 372 342 126
R2 0.255 0.434 0.589 0.197 0.421 0.325 0.479 0.655
R2 Adj. 0.168 0.367 0.541 0.102 0.353 0.246 0.411 0.577
RMSE 0.95 0.90 0.30 0.87 1.95 0.36 0.42 0.30

As with our national data, we do not see a statistically significant changes in social vulnerability or economic outcomes for tracts participating in the LIHTC program. We cannot conclude that the program had a measurable impact in the Mountain Division tracts.

Visualize Divisional Models

Because we do not have any statistically significant changes in our outcomes we not need to visualize the counterfactual and outcome slopegraphs.